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Visible light communication (VLC) system has been becoming a potential technology for short-range data communication over wireless optical channel. In this paper, a real-time state-of-the-art VLC system prototype is implemented mainly based on enhanced subcarrier index modulation orthogonal frequency division multiplexing (ESIM-OFDM) modulation scheme. The prototype has been designed using NI cDAQ hardware equipment programmed in MATLAB/LABVIEW. In other words, the developed VLC transceiver LABVIEW model is loaded into cDAQ to perform real-time transmission of text data. The design mainly comprises VLC transmitter, VLC receiver, interfacing NI cDAQ with LABVIEW, and implementation of ESIM-OFDM. It should be noted that the direct output of ESIM-OFDM is not suitable to drive VLC transmitter circuit; therefore, the key contribution lies in making use of sigma-delta modulator to modify ESIM-OFDM output to produce constant current signal which in turn could drive VLC transmitter. The second main contribution of the work targets the luminous properties of the designed transmitter that have experimentally been characterized using a LUX meter. Finally, the last contribution of the work is the simulation carried out to analyze the performance of the proposed system with other existing VLC modulation schemes.

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Face detection, which is an effortless task for humans, is complex to perform on machines. The recent veer proliferation of computational resources is paving the way for frantic advancement of face detection technology. Many astutely developed algorithms have been proposed to detect faces. However, there is little attention paid in making a comprehensive survey of the available algorithms. This paper aims at providing fourfold discussions on face detection algorithms. First, we explore a wide variety of the available face detection algorithms in five steps, including history, working procedure, advantages, limitations, and use in other fields alongside face detection. Secondly, we include a comparative evaluation among different algorithms in each single method. Thirdly, we provide detailed comparisons among the algorithms epitomized to have an all-inclusive outlook. Lastly, we conclude this study with several promising research directions to pursue. Earlier survey papers on face detection algorithms are limited to just technical details and popularly used algorithms. In our study, however, we cover detailed technical explanations of face detection algorithms and various recent sub-branches of the neural network. We present detailed comparisons among the algorithms in all-inclusive and under sub-branches. We provide the strengths and limitations of these algorithms and a novel literature survey that includes their use besides face detection.

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