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Author: search.filters.author.Alfarozi, Syukron Abu IshaqSubject: search.filters.subject.image sensor communication (ISC)

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    Item type:Publication,
    Square Wave Quadrature Amplitude Modulation for Visible Light Communication Using Image Sensor
    (2019-01-01)
    Alfarozi, Syukron Abu Ishaq
    ;
    Pasupa, Kitsuchart
    ;
    Hashizume, Hiromichi
    ;
    Woraratpanya, Kuntpong
    ;
    Sugimoto, Masanori
    Most visible light communication (VLC) technologies use a light emitting diode (LED) as a data transmitter and a photodiode as a receiver. In this paper, we alternatively focus on the use of an image sensor or camera as a receiver due to its wide availability. However, the successful use of an image sensor mainly depends on the efficiency of the encoder-decoder and the modulation scheme. Thus, this paper proposes a novel modulation scheme based on a square wave signal called a square wave quadrature amplitude modulation (SW-QAM) method. This method can accommodate different camera settings and overcome the problem of LED flicker that is generally sensed by human eyes when the LED frequency is low. At the transmitter side, multiple LEDs can be used to increase the transmission bit rate, while, at the receiver side, a Wiener filter is used as a complementary technique to SW-QAM for solving the light interference phenomenon due to the closeness of one LED to another. Our experimental results show that the proposed SW-QAM scheme can decode symbols very well either the for close or far communication distances, dark or bright lighting conditions, and single or multiple LED points.
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    Robust and Unified VLC Decoding System for Square Wave Quadrature Amplitude Modulation Using Deep Learning Approach
    (2019-01-01)
    Alfarozi, Syukron Abu Ishaq
    ;
    Pasupa, Kitsuchart
    ;
    Hashizume, Hiromichi
    ;
    Woraratpanya, Kuntpong
    ;
    Sugimoto, Masanori
    We have proposed a square wave quadrature amplitude modulation (SW-QAM) scheme for visible light communication (VLC) using an image sensor in our previous work. Here, we propose a robust and unified system by using a neural decoding method. This method offers essential SW-QAM decoding capabilities, such as LED localization, light interference elimination, and unknown parameter estimation, bundled into a single neural network model. This work makes use of a convolutional neural network (CNN) that has a capability in automatic learning of unknown parameters, especially when it deals with images as an input. The neural decoding method can provide good solutions for two difficult conditions that are not covered by our previous SW-QAM scheme: unfixed LED positions and multiple point spread functions (PSFs) of multiple LEDs. Responding to the above solutions, three recent CNN architectures - VGG, ResNet, and DenseNet - are modified to suit our scheme and other two small CNN architectures - VGG-like and MiniDenseNet - are proposed for low computing devices. Our experimental results show that the proposed neural decoding method performs better in terms of error rate than the theoretical decoding, an SW-QAM decoder with a $Wiener$ filter, in different scenarios. Furthermore, we experiment on the problem of moving camera, i.e., the unfixed position of LED points. For this case, a spatial transformer network (STN) layer is added to the neural decoding method for solving the moving camera problem, and the method with the new layer achieves a remarkable result.