모션 및 선택적 캡쳐를 적용한 효율적인 근거리 광카메라 통신

Abstract

With the advancements in smart device and mobile phone cameras and light emitting diodes (LEDs), optical camera communication (OCC), also termed camera-based visible light communication (CVLC) has emerged as a novel communication scheme for providing data communication. Using visible light, as the communication medium, VLC and OCC possess many advantages over conventional RF communications, such as large unlicensed spectral bandwidth, usability at radio frequency (RF) prohibited areas. The OCC technique is an extension of VLC with the advantage of no extended hardware cost of the receiver in most smart devices. Unlike conventional VLCs, which employ photodetectors (PDs), the OCC utilizes a mobile phone CMOS camera as the receiver. That is, OCC captures two-dimensional (2D) data in the form of image sequences, thus being able to transmit more information compared to photodetector-based VLCs. In order to exploit the applications to provide cost-effective and convenient smart home environments as well as high-speed data communication, this thesis addresses applications and important issues in indoor OCCs. Moreover, the thesis provides a fundamental analysis of the OCC system in aspects of illumination and data rate. In the first study, a flexible and novel motion detection scheme over a smart device camera in OCC is proposed. The motion detection is performed in conjunction with a static downlink OCC, where a mobile phone front camera is employed as the receiver and an 8×8 dot matrix LED as the transmitter. To provide illumination and communication, additional 10 white LEDs are employed which also helps in acquiring camera focus, and light metering. The motion detection or motion over camera (MoC) is designed to detect the user’s finger movement through the OCC link via the camera. A simple but efficient quadrant division based motion detection algorithm is proposed for accurate detection of motion. The experiment and simulation results demonstrate that the proposed scheme is able to detect motion with a success probability of up to 96 percent in the mobile phone camera based OCC. It is envisioned that the proposed motion detection can facilitate cost-effective and convenient smart home environments in the OCC, where the provision of illumination and short-range wireless communications has already been addressed. Secondly, the thesis presents a novel concept of trained neurons based motion detection (TNMD) in OCC. The TNMD is proposed in order to enhance the performance of the motion detection proposed in the first study for its realistic and practical implementations. The proposed TNMD is based on neurons present in a neural network (NN) that perform repetitive analysis in order to provide efficient and reliable motion detection in OCC. This efficient motion detection can be considered another functionality of OCC in addition to two traditional functionalities of illumination and communication. The motion is detected in the similar fashion as described in the first study, that is, by observing the user's finger movement in the form of centroid through the OCC link via a camera. Unlike conventionally trained neurons approaches, the proposed TNMD is trained not with motion itself but with centroid data samples, thus providing more accurate detection and far less complex detection algorithm. Experiment results demonstrate that the TNMD can detect all considered motions accurately with acceptable bit error rate (BER) performances at a transmission distance of up to 175 cm. The OCC with the proposed TNMD combined can be considered an efficient indoor OCC system that provides illumination, communication and motion detection in a convenient smart home environment. The last study in this thesis proposes a distinct capturing strategy called selective capture (SC) in order to achieve high-speed and flicker-free OCC based vehicle-to-vehicle (V2V) communication. In optical camera communication (OCC), much effort has focused on increasing data rate and communication distance by maximally utilizing spatial, frequency, intensity or color dimensions. The major challenge in OCC is low data transmission rate, due to the low sampling rate of a camera-based receiver, compared with high-speed modulation of light emitting diodes (LEDs). Due to the use of the SC technique for capturing the taillights in the form of data frames, the capture speed of RaspiCam is increased from 120 frames per second (fps) to 435 fps, yielding an efficient, high-speed and flicker-free OCC for V2V. The experiment results demonstrate that the proposed OCC for V2V using SC technique achieves acceptable bit error rate (BER) performance at a distance of up to 175 cm.