Convolutional neural networkscheme–based optical camera communication system for intelligent Internet of vehicles

Islam, Amirul; Hossan, Md. Tanvir; Jang, Yeong Min

doi:10.1177/1550147718770153

Cited by 45 publications

(24 citation statements)

References 58 publications

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“…In this case, the receiver can detect the source and decode it using a few consecutive frames. In [ 26 ], convolutional neural networks (CNN) are implemented to discriminate the transmitting source from unwanted active optical objects, such as streetlights in the case of vehicle-to-vehicle communications, with an average precision of 60% in natural night conditions, with an elapsed time of 13

, approximately.…”

Section: Proposed Systemmentioning

confidence: 99%

Design and Experimental Characterization of a Discovery and Tracking System for Optical Camera Communications

Mederos-Barrera

Jurado-Verdú

Guerra

et al. 2021

Sensors

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Visible light communications (VLC) technology is emerging as a candidate to meet the demand for interconnected devices’ communications. However, the costs of incorporating specific hardware into end-user devices slow down its market entry. Optical camera communication (OCC) technology paves the way by reusing cameras as receivers. These systems have generally been evaluated under static conditions, in which transmitting sources are recognized using computationally expensive discovery algorithms. In vehicle-to-vehicle networks and wearable devices, tracking algorithms, as proposed in this work, allow one to reduce the time required to locate a moving source and hence the latency of these systems, increasing the data rate by up to 2100%. The proposed receiver architecture combines discovery and tracking algorithms that analyze spatial features of a custom RGB LED transmitter matrix, highlighted in the scene by varying the cameras’ exposure time. By using an anchor LED and changing the intensity of the green LED, the receiver can track the light source with a slow temporal deterioration. Moreover, data bits sent over the red and blue channels do not significantly affect detection, hence transmission occurs uninterrupted. Finally, a novel experimental methodology to evaluate the evolution of the detection’s performance is proposed. With the analysis of the mean and standard deviation of novel K parameters, it is possible to evaluate the detected region-of-interest scale and centrality against the transmitter source’s ideal location.

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, approximately.…”

Section: Proposed Systemmentioning

confidence: 99%

Design and Experimental Characterization of a Discovery and Tracking System for Optical Camera Communications

Mederos-Barrera

Jurado-Verdú

Guerra

et al. 2021

Sensors

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show abstract

“…In the VLC structure LEDs are deployed for the dual purposes of lighting and communication. OCC is one of the promising technologies of OWC, whereby the light intensity of LED or OLED is modulated to transmit the data, but the receiver is a camera instead of photodiodes, unlike traditional VLC systems, and; thus, requires almost no additional implementation cost and hardware modifications [42][43][44][45].…”

Section: Vl-occ System Architecture and Applicationsmentioning

confidence: 99%

Experimental Demonstration of Single-Channel EEG Signal Using 32 × 32 Pixel OLED Screen and Camera

et al. 2019

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Currently, the radiofrequency (RF)-based wireless technology deployed in electroencephalography (EEG) to diagnose brain diseases suffers from frequency spectrum and electromagnetic interference, and might also have adverse effects on the health of patients and equipment used in hospitals, especially in RF-restricted zones like intensive care units (ICUs). Optical wireless communication (OWC), specifically visible light communication (VLC), is featured in 5G network to complement the radiofrequency (RF) technologies due to the fact that huge unlicensed bandwidth and available infrastructure, both indoor and outdoor, reduces the implementation cost. The conventional VLC systems deploy photodiodes as receivers, requiring hardware and infrastructure modifications in addition to smaller field of view (FOV), but the use of cameras reduce the infrastructure cost due to inbuilt filters and a wider FOV coverage gives the ability to scale a larger area. The wider FOV and the movement of camera rotation, without any additional adjustments to maintain the line-of-sight (LOS), allows the patient to be anywhere within the room and FOV. This paper demonstrates a novel healthcare system for EEG using visible light optical camera communication (VL-OCC), where a 32 × 32 pixel OLED screen acts as transmitter and the receiver section consists of several different cameras such as digital single-lens reflex camera (DLSR), android smartphone, and Thorlabs camera. The experiments were performed in LOS deploying on-off keying (OOK) modulation at several distant measurements to determine the system reliability and stability through bit error rate (BER) performance. The proposed system results depict that the DSLR camera outperforms the smartphone and Thorlabs cameras, as it is capable of transmitting an error free bit rate of 2.8 kbps at 30 fps up to 5.5 m.

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“…Note, the concept of D2D is one form of the multiple-input multiple-output system, where every pixel is transmitted and detected. Similarly, to allow transmission and reception of information under bad weather conditions, a convolution neural network (CNN)-based OCC was proposed in [ 29 ]. The CNN was used for classification and recognition of LED patterns and to decode the transmitted data streams even under an unclear state, where LED patterns are not visible to the camera due to blocking of the transmission path and/or weather conditions.…”

Section: Introductionmentioning

confidence: 99%

The Utilization of Artificial Neural Network Equalizer in Optical Camera Communications

Younus

Hassan

Ghassemlooy

et al. 2021

Sensors

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In this paper, we propose and validate an artificial neural network-based equalizer for the constant power 4-level pulse amplitude modulation in an optical camera communications system. We introduce new terminology to measure the quality of the communications link in terms of the number of row pixels per symbol Npps, which allows a fair comparison considering the progress made in the development of the current image sensors in terms of the frame rates and the resolutions of each frame. Using the proposed equalizer, we experimentally demonstrate a non-flickering system using a single light-emitting diode (LED) with Npps of 20 and 30 pixels/symbol for the unequalized and equalized systems, respectively. Potential transmission rates of up to 18.6 and 24.4 kbps are achieved with and without the equalization, respectively. The quality of the received signal is assessed using the eye-diagram opening and its linearity and the bit error rate performance. An acceptable bit error rate (below the forward error correction limit) and an improvement of ~66% in the eye linearity are achieved using a single LED and a typical commercial camera with equalization.

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Convolutional neural networkscheme–based optical camera communication system for intelligent Internet of vehicles

Cited by 45 publications

References 58 publications

Design and Experimental Characterization of a Discovery and Tracking System for Optical Camera Communications

Design and Experimental Characterization of a Discovery and Tracking System for Optical Camera Communications

Experimental Demonstration of Single-Channel EEG Signal Using 32 × 32 Pixel OLED Screen and Camera

The Utilization of Artificial Neural Network Equalizer in Optical Camera Communications

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