Partha Pratim Banik | Khulna University of Engineering and Technology (original) (raw)
Papers by Partha Pratim Banik
2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT), 2019
For daily health checkup, wearable device plays an important role in healthcare applications. In ... more For daily health checkup, wearable device plays an important role in healthcare applications. In this manuscript, we develop a visual multiple-input multiple-output (visual-MIMO) system for healthcare application. As a demo of healthcare device, we design a pulse oximeter device that can measure heart rate (HR) and blood oxygenation (BO) of a person. The HR and BO data are used to send through light-emitting diode (LED) to camera communication. We have used generalized color modulation (GCM) technique to modulate the HR and BO data into the LED array for transmission and smartphone camera is used as the receiver to demodulate the transmitted data. We demonstrate our proposed visual-MIMO system by developing an android app on smartphone.
2018 IEEE International Conference on Consumer Electronics (ICCE), 2018
A low dynamic range (LDR) image that contains highlight does not provide proper information at th... more A low dynamic range (LDR) image that contains highlight does not provide proper information at the highlight area. Besides, all area of the image may not be exposed properly. Removing the highlight and then converting the image to the high dynamic range (HDR) image will increase the quality of the image from visual perception. In this paper, we propose a method for removing the highlight from an LDR image and convert the highlight free image to HDR image by using tone mapping operator (TMO). After detecting highlight area of an image, modified specular free (MSF) image is used to remove highlight part from the LDR image. Then, the highlight free LDR image is converted to HDR image by TMO. Finally, we have measured the quality of our output image to show that output image has better dynamic range than the input image.
Electronics, 2018
Hardware suitability of an algorithm can only be verified when the algorithm is actually implemen... more Hardware suitability of an algorithm can only be verified when the algorithm is actually implemented in the hardware. By hardware, we indicate system on chip (SoC) where both processor and field-programmable gate array (FPGA) are available. Our goal is to develop a simple algorithm that can be implemented on hardware where high-level synthesis (HLS) will reduce the tiresome work of manual hardware description language (HDL) optimization. We propose an algorithm to achieve high dynamic range (HDR) image from a single low dynamic range (LDR) image. We use highlight removal technique for this purpose. Our target is to develop parameter free simple algorithm that can be easily implemented on hardware. For this purpose, we use statistical information of the image. While software development is verified with state of the art, the HLS approach confirms that the proposed algorithm is implementable to hardware. The performance of the algorithm is measured using four no-reference metrics. Acc...
2018 IEEE International Conference on Consumer Electronics (ICCE), 2018
High dynamic range (HDR) of an image will increase the visibility of the image. Due to the reflec... more High dynamic range (HDR) of an image will increase the visibility of the image. Due to the reflection, the image may lose its visibility in some specific area. In the image, we call it highlight. We cannot reveal the original color and information in the highlight area. By removing this highlight, we can increase the visibility of the image that expands the dynamic range of the image. In this paper, we convert our algorithm by high-level synthesis (HLS) tool to the synthesizable hardware language. Our target is to show that our algorithm for HDR-like image can be implemented on FPGA. We also compare our HLS results with simulation result.
2019 International Conference on Electronics, Information, and Communication (ICEIC), 2019
Due to the higher exposure, the information in the highlight area usually clipped. This is one of... more Due to the higher exposure, the information in the highlight area usually clipped. This is one of the bottlenecks for the generation of high dynamic range (HDR) image. In this paper, we describe a simple technique to retrieve the information from an overexposed area by creating low dynamic range (LDR) image set from a single LDR image. These images are fused to generate HDR image. We evaluate our result by the single image HDR generation technique. Our technique shows better visual results compared to other techniques.
2019 25th Asia-Pacific Conference on Communications (APCC), 2019
HDR image reveals the fine details of LDR image. In this paper, we estimate the illumination map ... more HDR image reveals the fine details of LDR image. In this paper, we estimate the illumination map by fusing the illumination of long-exposure like reflectance, and short-exposure like LDR images. Then, we generate HDR image by using the illumination of fused and input LDR image. We evaluate our method by using three no-reference quality metric; histogram balance (HB), natural image quality evaluator (NIQE), and colorfulness-based patch-based contrast quality index (CPCQI). We compare our method with two reverse tone mapping methods. We show that our proposed method achieves the best result in terms of visual experience than other methods.
Modern image sensors can acquire more data than previous due to increased bit depth. But storage ... more Modern image sensors can acquire more data than previous due to increased bit depth. But storage devices and display devices do not support up to that bit depth. Alongside with this, to produce or capture HDR images it is also important to preserve the sensor native data as much as it can. These intentions motivate the introduction of log-encoding idea in imaging devices. Log-encoding saves storage as well as keeps roughly exact information from image sensor. Different camera manufacturing companies developed their own log-curve and color gamut for their products. In this paper, we discuss about the S-log curve which was developed by Sony. The discussion includes S-log curve types, workflow, example of output images and color gamut in brief.
Electronics, 2020
Clinical devices play a vital role in diagnosing and monitoring people’s health. A pulse oximeter... more Clinical devices play a vital role in diagnosing and monitoring people’s health. A pulse oximeter (PO) is one of the most common clinical devices for critical medical care. In this paper, we explain how we developed a wearable PO. We propose a new electronic circuit based on an analog filter that can separate red and green photoplethysmography (PPG) signals, acquire clean PPG signals, and estimate the pulse rate (PR) and peripheral capillary oxygen saturation (SpO2). We propose a PR and SpO2 measurement algorithm with and without the motion artifact. We consider three types of motion artifacts with our acquired clean PPG signal from our proposed electronic circuit. To evaluate our proposed algorithm, we measured the accuracy of our estimated SpO2 and PR. To evaluate the quality of our estimated PR (bpm) and SpO2 (%) with and without the finger motion artifact, we used the quality evaluation metrics: mean absolute percentage error (MAPE), mean absolute error (MAE), and reference clos...
2017 IEEE Globecom Workshops (GC Wkshps), 2017
The color detection from light emitting diode (LED) array by using smartphone camera is very diff... more The color detection from light emitting diode (LED) array by using smartphone camera is very difficult in visual multiple-input multiple-output (visual-MIMO) system. In this paper, we propose a method to detect the LED color through smartphone camera applying regression analysis. We apply multivariate regression model for detecting LED color. By taking picture of LED array, we detect the region of interest (ROI), then detecting LEDs by image processing algorithm, applying k means-clustering algorithm for generating possible number of colors of LED array and finally applying regression model to predict transmitted LEDs color. We get the value of test database R-squared 91.14%, 94.80% and 96.24% for red, green and blue channel, respectively.
2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), 2019
Blood cell image classification is an important part for medical diagnosis system. In this paper,... more Blood cell image classification is an important part for medical diagnosis system. In this paper, we propose a fused convolutional neural network (CNN) model to classify the images of white blood cell (WBC). We use five convolutional layer, three max-pooling layer and a fully connected network with single hidden layer. We fuse the feature maps of two convolutional layers by using the operation of max-pooling to give input to the fully connected neural network layer. We compare the result of our model accuracy and computational time with CNN-recurrent neural network (RNN) combined model. We also show that our model trains faster than CNN-RNN model.
IET Image Processing, 2020
Low dynamic range (LDR) image may contain low-light and highlight areas due to the limitations of... more Low dynamic range (LDR) image may contain low-light and highlight areas due to the limitations of the dynamic range of conventional image sensors. Low-light and highlight phenomena limit colour richness and visibility of objects in an image. Therefore, it can cause a reduction in the quality of images and a loss in accuracy in the application of image recognition. To overcome this, high dynamic range (HDR)-like images have been developed with rich colours such as those seen by the human eye. In this study, the authors propose a method to obtain an HDR-like image from a single LDR image by removing the specular component from highlight pixels as well as strengthening the actual colour. Next, they select low-light image enhancement via illumination map estimation as a low-light enhancement technique by showing the comparison with gamma-based expansion operator. They evaluate their HDR-like output images with non-reference and full-reference metrics. They show the comparison of their proposed method with six other methods. Besides, visually, their proposed method delivers more pleasing output than the output of other competitive methods.
Expert Systems with Applications, 2020
Abstract White blood cells (WBCs) play a remarkable role in the human immune system. To diagnose ... more Abstract White blood cells (WBCs) play a remarkable role in the human immune system. To diagnose blood-related diseases, pathologists need to consider the characteristics of WBC. The characteristics of WBC can be defined based on the morphological properties of WBC nucleus. Therefore, nucleus segmentation plays a vital role to classify the WBC image and it is an important part of the medical diagnosis system. In this study, color space conversion and k-means algorithm based new WBC nucleus segmentation method is proposed. Then we localize the WBC based on the location of segmented nucleus to separate them from the entire blood smear image. To classify the localized WBC image, we propose a new convolutional neural network (CNN) model by combining the concept of fusing the features of first and last convolutional layers, and propagating the input image to the convolutional layer. We also use a dropout layer for preventing the model from overfitting problem. We show the effectiveness of our proposed nucleus segmentation method by evaluating with seven quality metrics and comparing with other methods on four public databases. We achieve average accuracy of 98.61% and more than 97% on each public database. We also evaluate our proposed CNN model by using nine classification metrics and achieve an overall accuracy of 96% on BCCD test database. To validate the generalization capability of our proposed CNN model, we show the training and testing accuracy and loss curves for random test set of BCCD database. Further, we compare the performance of our proposed CNN model with four state-of-the-art CNN models (biomedical image classifier) by measuring the value of evaluation metrics.
2018 International Conference on Electronics, Information, and Communication (ICEIC), 2018
Low-light image contains compressed dynamic range that can be enhanced for knowing detail informa... more Low-light image contains compressed dynamic range that can be enhanced for knowing detail information. Contrast enhancement of low-light image is a challenging task in image processing field. In this paper, we enhance the different type of low-light image by using histogram equalization (HE) and illumination adjustment. We present a method to detect different types of low-light images. Then, we apply the HE on V channel of input low-light image after converting the color space from RGB to HSV. After that, we enhance the contrast of V by adjusting intensity (V) of low-light image with adopting gamma correction. We evaluate our low-light enhanced method by naturalness image quality evaluator (NIQE) and colorfulness-based patch-based contrast quality index (CPCQI) and also compare our proposed method with typical HE method by measuring quality of images.
Optics Communications, 2018
Color detection from a light emitting diode (LED) array using a smartphone camera is very difficu... more 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.
Optics Communications, 2018
Abstract Color decision of Light-emitting diode (LED) by smartphone cameras is a challenging area... more Abstract 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.
2015 International Conference on Advances in Electrical Engineering (ICAEE), 2015
Now a days mobile phone has become a part of our daily life. Due to low cost of mobile phones, mo... more Now a days mobile phone has become a part of our daily life. Due to low cost of mobile phones, mobile phones are widely used for home automation. In this paper a remotely operated mobile phone controlled home appliances system is proposed. It is a DTMF (dual- tone multiple-frequency) based system consists of two mobile phones, DTMF decoder and ATmega8 microcontroller. One mobile phone is used as remote which may locate at far distance from home and another mobile phone is located at the home which acts as a receiver. The control information are sent via the remote mobile phone as DTMF tone, this DTMF tone is received by the mobile phone located at home, the received DTMF tone is then decoded by DTMF decoder MT8870 IC. The output logic signal of the decoder is used as input to the ATmea8 microcontroller. The ATmega8 microcontroller is previously programmed to control home appliances according to output of the DTMF decoder.
2015 International Conference on Electrical Engineering and Information Communication Technology (ICEEICT), 2015
ABSTRACT
2018 Tenth International Conference on Ubiquitous and Future Networks (ICUFN), 2018
LED color detection is a vital part in visual-MIMO system. For deciding transmitted symbols from ... more LED color detection is a vital part in visual-MIMO system. For deciding transmitted symbols from an LED array image, it is important to detect the color of LED on receiver side. In this paper, we propose a training algorithm, called boosting neural network (BNN) to predict the color of LED on receiver side. First, we take the image of LED array and segment the LED image by using LED detection algorithm. After segmenting the LED image, the LED image is resized in 10 by 10 dimension that means 100 pixels. Each pixel is the input to the BNN model for each RGB color channel. For studying the behavior of each color LED image in low (565 lux) and strong (2450 lux) environmental light intensity, we train our BNN model for low and strong environmental light intensity. Finally, we compare the performance of our BNN model with the regression analysis model at low and strong environmental light intensity. We obtain greater closeness accuracy for each color channel at both environmental light intensities.
Most of the paralyzed or physically disabled persons are unable to communicate with others, easil... more Most of the paralyzed or physically disabled persons are unable to communicate with others, easily. To minimize this problem, different types of Human-Computer Interface (HCI) systems have been developed in recent years. In this paper, a single channel electrooculography (EOG) based HCI system has been proposed to increase the communication ability as well as quality of life for paralyzed persons who cannot speak or move their limbs. The extracted EOG signals are processed by our EOG acquisition system and sent it to a microcontroller unit which processes those signals for interfacing with computer via serial communication. A Graphical User Interface (GUI) is designed using MATLAB which contains some buttons to help a user to express what he/she wants through messages. A liquid crystal display (LCD) is used to show the messages. Our experimental results show that the maximum and minimum average time recorded for selecting 10 buttons for a particular user are 4.27 and 4. 11 second, respectively. Particularly for selecting a button, the maximum and minimum average time recorded by every user are respectively 5.58 second and 1.82 second. We have found that the average button selection accuracy is around 95%.
2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT), 2019
For daily health checkup, wearable device plays an important role in healthcare applications. In ... more For daily health checkup, wearable device plays an important role in healthcare applications. In this manuscript, we develop a visual multiple-input multiple-output (visual-MIMO) system for healthcare application. As a demo of healthcare device, we design a pulse oximeter device that can measure heart rate (HR) and blood oxygenation (BO) of a person. The HR and BO data are used to send through light-emitting diode (LED) to camera communication. We have used generalized color modulation (GCM) technique to modulate the HR and BO data into the LED array for transmission and smartphone camera is used as the receiver to demodulate the transmitted data. We demonstrate our proposed visual-MIMO system by developing an android app on smartphone.
2018 IEEE International Conference on Consumer Electronics (ICCE), 2018
A low dynamic range (LDR) image that contains highlight does not provide proper information at th... more A low dynamic range (LDR) image that contains highlight does not provide proper information at the highlight area. Besides, all area of the image may not be exposed properly. Removing the highlight and then converting the image to the high dynamic range (HDR) image will increase the quality of the image from visual perception. In this paper, we propose a method for removing the highlight from an LDR image and convert the highlight free image to HDR image by using tone mapping operator (TMO). After detecting highlight area of an image, modified specular free (MSF) image is used to remove highlight part from the LDR image. Then, the highlight free LDR image is converted to HDR image by TMO. Finally, we have measured the quality of our output image to show that output image has better dynamic range than the input image.
Electronics, 2018
Hardware suitability of an algorithm can only be verified when the algorithm is actually implemen... more Hardware suitability of an algorithm can only be verified when the algorithm is actually implemented in the hardware. By hardware, we indicate system on chip (SoC) where both processor and field-programmable gate array (FPGA) are available. Our goal is to develop a simple algorithm that can be implemented on hardware where high-level synthesis (HLS) will reduce the tiresome work of manual hardware description language (HDL) optimization. We propose an algorithm to achieve high dynamic range (HDR) image from a single low dynamic range (LDR) image. We use highlight removal technique for this purpose. Our target is to develop parameter free simple algorithm that can be easily implemented on hardware. For this purpose, we use statistical information of the image. While software development is verified with state of the art, the HLS approach confirms that the proposed algorithm is implementable to hardware. The performance of the algorithm is measured using four no-reference metrics. Acc...
2018 IEEE International Conference on Consumer Electronics (ICCE), 2018
High dynamic range (HDR) of an image will increase the visibility of the image. Due to the reflec... more High dynamic range (HDR) of an image will increase the visibility of the image. Due to the reflection, the image may lose its visibility in some specific area. In the image, we call it highlight. We cannot reveal the original color and information in the highlight area. By removing this highlight, we can increase the visibility of the image that expands the dynamic range of the image. In this paper, we convert our algorithm by high-level synthesis (HLS) tool to the synthesizable hardware language. Our target is to show that our algorithm for HDR-like image can be implemented on FPGA. We also compare our HLS results with simulation result.
2019 International Conference on Electronics, Information, and Communication (ICEIC), 2019
Due to the higher exposure, the information in the highlight area usually clipped. This is one of... more Due to the higher exposure, the information in the highlight area usually clipped. This is one of the bottlenecks for the generation of high dynamic range (HDR) image. In this paper, we describe a simple technique to retrieve the information from an overexposed area by creating low dynamic range (LDR) image set from a single LDR image. These images are fused to generate HDR image. We evaluate our result by the single image HDR generation technique. Our technique shows better visual results compared to other techniques.
2019 25th Asia-Pacific Conference on Communications (APCC), 2019
HDR image reveals the fine details of LDR image. In this paper, we estimate the illumination map ... more HDR image reveals the fine details of LDR image. In this paper, we estimate the illumination map by fusing the illumination of long-exposure like reflectance, and short-exposure like LDR images. Then, we generate HDR image by using the illumination of fused and input LDR image. We evaluate our method by using three no-reference quality metric; histogram balance (HB), natural image quality evaluator (NIQE), and colorfulness-based patch-based contrast quality index (CPCQI). We compare our method with two reverse tone mapping methods. We show that our proposed method achieves the best result in terms of visual experience than other methods.
Modern image sensors can acquire more data than previous due to increased bit depth. But storage ... more Modern image sensors can acquire more data than previous due to increased bit depth. But storage devices and display devices do not support up to that bit depth. Alongside with this, to produce or capture HDR images it is also important to preserve the sensor native data as much as it can. These intentions motivate the introduction of log-encoding idea in imaging devices. Log-encoding saves storage as well as keeps roughly exact information from image sensor. Different camera manufacturing companies developed their own log-curve and color gamut for their products. In this paper, we discuss about the S-log curve which was developed by Sony. The discussion includes S-log curve types, workflow, example of output images and color gamut in brief.
Electronics, 2020
Clinical devices play a vital role in diagnosing and monitoring people’s health. A pulse oximeter... more Clinical devices play a vital role in diagnosing and monitoring people’s health. A pulse oximeter (PO) is one of the most common clinical devices for critical medical care. In this paper, we explain how we developed a wearable PO. We propose a new electronic circuit based on an analog filter that can separate red and green photoplethysmography (PPG) signals, acquire clean PPG signals, and estimate the pulse rate (PR) and peripheral capillary oxygen saturation (SpO2). We propose a PR and SpO2 measurement algorithm with and without the motion artifact. We consider three types of motion artifacts with our acquired clean PPG signal from our proposed electronic circuit. To evaluate our proposed algorithm, we measured the accuracy of our estimated SpO2 and PR. To evaluate the quality of our estimated PR (bpm) and SpO2 (%) with and without the finger motion artifact, we used the quality evaluation metrics: mean absolute percentage error (MAPE), mean absolute error (MAE), and reference clos...
2017 IEEE Globecom Workshops (GC Wkshps), 2017
The color detection from light emitting diode (LED) array by using smartphone camera is very diff... more The color detection from light emitting diode (LED) array by using smartphone camera is very difficult in visual multiple-input multiple-output (visual-MIMO) system. In this paper, we propose a method to detect the LED color through smartphone camera applying regression analysis. We apply multivariate regression model for detecting LED color. By taking picture of LED array, we detect the region of interest (ROI), then detecting LEDs by image processing algorithm, applying k means-clustering algorithm for generating possible number of colors of LED array and finally applying regression model to predict transmitted LEDs color. We get the value of test database R-squared 91.14%, 94.80% and 96.24% for red, green and blue channel, respectively.
2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), 2019
Blood cell image classification is an important part for medical diagnosis system. In this paper,... more Blood cell image classification is an important part for medical diagnosis system. In this paper, we propose a fused convolutional neural network (CNN) model to classify the images of white blood cell (WBC). We use five convolutional layer, three max-pooling layer and a fully connected network with single hidden layer. We fuse the feature maps of two convolutional layers by using the operation of max-pooling to give input to the fully connected neural network layer. We compare the result of our model accuracy and computational time with CNN-recurrent neural network (RNN) combined model. We also show that our model trains faster than CNN-RNN model.
IET Image Processing, 2020
Low dynamic range (LDR) image may contain low-light and highlight areas due to the limitations of... more Low dynamic range (LDR) image may contain low-light and highlight areas due to the limitations of the dynamic range of conventional image sensors. Low-light and highlight phenomena limit colour richness and visibility of objects in an image. Therefore, it can cause a reduction in the quality of images and a loss in accuracy in the application of image recognition. To overcome this, high dynamic range (HDR)-like images have been developed with rich colours such as those seen by the human eye. In this study, the authors propose a method to obtain an HDR-like image from a single LDR image by removing the specular component from highlight pixels as well as strengthening the actual colour. Next, they select low-light image enhancement via illumination map estimation as a low-light enhancement technique by showing the comparison with gamma-based expansion operator. They evaluate their HDR-like output images with non-reference and full-reference metrics. They show the comparison of their proposed method with six other methods. Besides, visually, their proposed method delivers more pleasing output than the output of other competitive methods.
Expert Systems with Applications, 2020
Abstract White blood cells (WBCs) play a remarkable role in the human immune system. To diagnose ... more Abstract White blood cells (WBCs) play a remarkable role in the human immune system. To diagnose blood-related diseases, pathologists need to consider the characteristics of WBC. The characteristics of WBC can be defined based on the morphological properties of WBC nucleus. Therefore, nucleus segmentation plays a vital role to classify the WBC image and it is an important part of the medical diagnosis system. In this study, color space conversion and k-means algorithm based new WBC nucleus segmentation method is proposed. Then we localize the WBC based on the location of segmented nucleus to separate them from the entire blood smear image. To classify the localized WBC image, we propose a new convolutional neural network (CNN) model by combining the concept of fusing the features of first and last convolutional layers, and propagating the input image to the convolutional layer. We also use a dropout layer for preventing the model from overfitting problem. We show the effectiveness of our proposed nucleus segmentation method by evaluating with seven quality metrics and comparing with other methods on four public databases. We achieve average accuracy of 98.61% and more than 97% on each public database. We also evaluate our proposed CNN model by using nine classification metrics and achieve an overall accuracy of 96% on BCCD test database. To validate the generalization capability of our proposed CNN model, we show the training and testing accuracy and loss curves for random test set of BCCD database. Further, we compare the performance of our proposed CNN model with four state-of-the-art CNN models (biomedical image classifier) by measuring the value of evaluation metrics.
2018 International Conference on Electronics, Information, and Communication (ICEIC), 2018
Low-light image contains compressed dynamic range that can be enhanced for knowing detail informa... more Low-light image contains compressed dynamic range that can be enhanced for knowing detail information. Contrast enhancement of low-light image is a challenging task in image processing field. In this paper, we enhance the different type of low-light image by using histogram equalization (HE) and illumination adjustment. We present a method to detect different types of low-light images. Then, we apply the HE on V channel of input low-light image after converting the color space from RGB to HSV. After that, we enhance the contrast of V by adjusting intensity (V) of low-light image with adopting gamma correction. We evaluate our low-light enhanced method by naturalness image quality evaluator (NIQE) and colorfulness-based patch-based contrast quality index (CPCQI) and also compare our proposed method with typical HE method by measuring quality of images.
Optics Communications, 2018
Color detection from a light emitting diode (LED) array using a smartphone camera is very difficu... more 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.
Optics Communications, 2018
Abstract Color decision of Light-emitting diode (LED) by smartphone cameras is a challenging area... more Abstract 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.
2015 International Conference on Advances in Electrical Engineering (ICAEE), 2015
Now a days mobile phone has become a part of our daily life. Due to low cost of mobile phones, mo... more Now a days mobile phone has become a part of our daily life. Due to low cost of mobile phones, mobile phones are widely used for home automation. In this paper a remotely operated mobile phone controlled home appliances system is proposed. It is a DTMF (dual- tone multiple-frequency) based system consists of two mobile phones, DTMF decoder and ATmega8 microcontroller. One mobile phone is used as remote which may locate at far distance from home and another mobile phone is located at the home which acts as a receiver. The control information are sent via the remote mobile phone as DTMF tone, this DTMF tone is received by the mobile phone located at home, the received DTMF tone is then decoded by DTMF decoder MT8870 IC. The output logic signal of the decoder is used as input to the ATmea8 microcontroller. The ATmega8 microcontroller is previously programmed to control home appliances according to output of the DTMF decoder.
2015 International Conference on Electrical Engineering and Information Communication Technology (ICEEICT), 2015
ABSTRACT
2018 Tenth International Conference on Ubiquitous and Future Networks (ICUFN), 2018
LED color detection is a vital part in visual-MIMO system. For deciding transmitted symbols from ... more LED color detection is a vital part in visual-MIMO system. For deciding transmitted symbols from an LED array image, it is important to detect the color of LED on receiver side. In this paper, we propose a training algorithm, called boosting neural network (BNN) to predict the color of LED on receiver side. First, we take the image of LED array and segment the LED image by using LED detection algorithm. After segmenting the LED image, the LED image is resized in 10 by 10 dimension that means 100 pixels. Each pixel is the input to the BNN model for each RGB color channel. For studying the behavior of each color LED image in low (565 lux) and strong (2450 lux) environmental light intensity, we train our BNN model for low and strong environmental light intensity. Finally, we compare the performance of our BNN model with the regression analysis model at low and strong environmental light intensity. We obtain greater closeness accuracy for each color channel at both environmental light intensities.
Most of the paralyzed or physically disabled persons are unable to communicate with others, easil... more Most of the paralyzed or physically disabled persons are unable to communicate with others, easily. To minimize this problem, different types of Human-Computer Interface (HCI) systems have been developed in recent years. In this paper, a single channel electrooculography (EOG) based HCI system has been proposed to increase the communication ability as well as quality of life for paralyzed persons who cannot speak or move their limbs. The extracted EOG signals are processed by our EOG acquisition system and sent it to a microcontroller unit which processes those signals for interfacing with computer via serial communication. A Graphical User Interface (GUI) is designed using MATLAB which contains some buttons to help a user to express what he/she wants through messages. A liquid crystal display (LCD) is used to show the messages. Our experimental results show that the maximum and minimum average time recorded for selecting 10 buttons for a particular user are 4.27 and 4. 11 second, respectively. Particularly for selecting a button, the maximum and minimum average time recorded by every user are respectively 5.58 second and 1.82 second. We have found that the average button selection accuracy is around 95%.