We put forward and experimentally demonstrate a second order machinelearning (ML) based visible-light-positioning (VLP) system using simple linear interpolation algorithm to reduce the training samples required in the ML algorithm. Algorithms of the second order regression ML model using 2,430 training samples; and using the reduced training samples of 570 with and without the proposed linear interpolation are compared and discussed. We can observe that the positioning accuracy of using training samples of 570 with the proposed interpolation can have similar performance when compared with using 2,430 training samples. The training samples are reduced by ∼76.5%. Here, off-the-shelf LED lamps and low bandwidth electrical and optical components are employed; and the system is cost-effective. Good quality on-off keying (OOK) identifier (ID) signals are retrieved after frequency down-conversion from 20 kHz, 40 kHz and 60 kHz without and with optical background noises respectively.

We put forward and demonstrate a angle-of-arrival (AOA) based visible-lightpositioning (VLP) system using quadrant-solar-cell (QSC) and third-order ridge regression machine learning (RRML) to improve the positioning accuracy.

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We propose and demonstrate using Z-score averaging neural-network (Z-NN) and background-content-removal (BCR) to mitigate the inter-symbol-interference (ISI) in camera-communication (OCC). 950-bit/s over 3-m free-space transmission is achieved.

We propose and demonstrate a received-signal-strength (RSS) based visible light positioning (VLP) system using a low-cost organic photovoltaic cell (OPVC) receiver (Rx). The OPVC is a passive device without the need of external power supply. It could detect VLC signal and harvest energy. Our developed OPVC has a high power conversion efficiency (PCE) of 9.8%. The VLP system can be operated at a low illumination of 130 lux. The regression machine learning (ML) algorithm is used to enhance the positioning accuracy.