Increasing data rate and security by blue-pixel-value adaptive thresholding for smartphone-screen-based blue-light optical camera communication

Embedding optical camera communication (OCC) commercially as a favorable complement of radio-frequency technology has led to the desire for an intelligent receiver system that is eligible to communicate with an accurate light-emitting diode (LED) transmitter. To shed light on this issue, a novel scheme for detecting and recognizing data transmitting LEDs has been elucidated in this paper. Since the optically modulated signal is captured wirelessly by a camera that plays the role of the receiver for the OCC technology, the process to detect LED region and retrieval of exact information from the image sensor is required to be intelligent enough to achieve a low bit error rate (BER) and high data rate to ensure reliable optical communication within limited computational abilities of the most used commercial cameras such as those in smartphones, vehicles, and mobile robots. In the proposed scheme, we have designed an intelligent camera receiver system that is capable of separating accurate data transmitting LED regions removing other unwanted LED regions employing a support vector machine (SVM) classifier along with a convolutional neural network (CNN) in the camera receiver. CNN is used to detect every LED region from the image frame and then essential features are extracted to feed into an SVM classifier for further accurate classification. The receiver operating characteristic curve and other key performance parameters of the classifier have been analyzed broadly to evaluate the performance, justify the assistance of the SVM classifier in recognizing the accurate LED region, and decode data with low BER. To investigate communication performances, BER analysis, data rate, and inter-symbol interference have been elaborately demonstrated for the proposed intelligent receiver. In addition, BER against distance and BER against data rate have also been exhibited to validate the effectiveness of our proposed scheme comparing with only CNN and only SVM classifier based receivers individually. Experimental results have ensured the robustness and applicability of the proposed scheme both in the static and mobile scenarios.

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Performance Analysis and Improvement of Optical Camera Communication

Applied Sciences ◽

10.3390/app8122527 ◽

2018 ◽

Vol 8 (12) ◽

pp. 2527 ◽

Cited By ~ 12

Author(s):

Moh. Khalid Hasan ◽

Mostafa Zaman Chowdhury ◽

Md. Shahjalal ◽

Van Thang Nguyen ◽

Yeong Min Jang

Keyword(s):

User Satisfaction ◽

High Performance ◽

Focal Length ◽

Sampling Rate ◽

Image Sensor ◽

Edge Length ◽

Data Rate ◽

Principal Factors ◽

Communication Distance ◽

Optical Camera Communication

Optical camera communication (OCC) is a technology in which a camera image sensor is employed to receive data bits sent from a light source. OCC has attracted a lot of research interest in the area of mobile optical wireless communication due to the popularity of smartphones with embedded cameras. Moreover, OCC offers high-performance characteristics, including an excellent signal-to-interference-plus-noise ratio (SINR), high security, low interference, and high stability with respect to varying communication distances. Despite these advantages, OCC suffers from several limitations, the primary of which is the low data rate. In this paper, we provide a comprehensive analysis of the parameters that influence the OCC performance. These parameters include the camera sampling rate, the exposure time, the focal length, the pixel edge length, the transmitter configurations, and the optical flickering rate. In particular, the focus is on enhancing the data rate, SINR, and communication distance, which are the principal factors determining the quality of service experienced by a user. The paper also provides a short survey of modulation schemes used in OCC on the basis of the achieved data rate, communication distance, and possible application scenarios. A theoretical analysis of user satisfaction using OCC is also rendered. Furthermore, we present the simulation results demonstrating OCC performance with respect to variations in the parameters mentioned above, which include the outage probability analysis for OCC.

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Enhancer for ovarian cyst segmentation using adaptive thresholding technique

Indian Journal of Science and Technology ◽

10.17485/ijst/v13i39.1602 ◽

2020 ◽

Vol 13 (39) ◽

pp. 4142-4150

Author(s):

S Sheela

Keyword(s):

Ovarian Cyst ◽

Matthews Correlation Coefficient ◽

Similarity Index ◽

Adaptive Thresholding ◽

Ultrasound Images ◽

Jaccard Similarity ◽

Segmented Image ◽

Pixel Value ◽

Sensitivity Specificity

Objective: To achieve the accurate segmentation of ovarian cyst from the ultrasound images. Method: Ovarian cyst ultrasound images are taken from ultrasound images.com and sonoworld.com. The cysts are segmented using adaptive thresholding technique. The segmented image (binary image) is divided into sub blocks and then number of binary transition in each block is calculated. Based on the number of transition, the pixel values are replaced by 0 or the same pixel value is maintained. In order to measure the performance of the proposed enhancer various measures like Accuracy (ACC), Dice Coefficient (DC), Jaccard Similarity Index (JSI), Matthews correlation coefficient (MCC), Sensitivity, Specificity and Precision are measured. Findings: In order to analyse the performance of the enhancer with adaptive thresholding technique, 100 ultrasound ovarian cyst images are taken. The enhancer produced better result than the existing adaptive thresholding technique. Novelty/Application: The proposed enhancer enriches the quality of the ovarian cyst segmentation.

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