In this letter, we propose an indoor visible light positioning technique that combines deep neural network based on the Bayesian Regularization (BR-DNN) with sparse diagonal training data set. Unlike other neural networks, which require a large number of training data points to locate accurately, we realize the high precision positioning with only 20 training points in a $\rm 1.8m\times 1.8m\times 2.1m$ location area. Furthermore, we test a new optimization method of training data set which is the diagonal set. To verify our ideas, we experimentally demonstrate three different training data acquisition methods which contain the common choice of training points (even set), arbitrary selection (arbitrary set) and diagonal selection (diagonal set). Experimental results show that the average localization accuracy optimized by the BR-DNN is 3.40 cm with diagonal set, while the average localization accuracy is 4.35 cm for the arbitrary set and 4.58cm for the even set. In addition, the training time and positioning time are only 11.25ms and 8.66ms due to significant reduction of the sparse training data. All of above experimental results show that the algorithm and training data optimization we proposed provide a new solution for real-time and high accuracy positioning with neural network.

We demonstrate a Gb/s real-time visible light communication (VLC) system based on NRZ-OOK by a phosphorescent white light-emitting diode (LED). We propose an equalizer employed by an RC series-parallel connection cascade circuit based on the T-bridge structure. With the proposed circuit, the 3-dB bandwidth of the VLC system can be extended from 1 to 520 MHz. The Bit Error Ratio (BER) of the system was 7.36 × 10 ^-4 , and the data rate was 1 Gb/s at a distance of 1.5 m. To the best of our knowledge, this is the highest real-time data rate and 3-dB bandwidth achieved based on a white LED.

In this letter, we propose an indoor visible light positioning technique using a Modified Momentum Back-Propagation (MMBP) algorithm based on received signal strength (RSS) with sparse training data set. Unlike other neural network algorithms that require a large number of training data points to locate accurately, we have realized high-precision positioning for 100 test points with only 20 training points in a 1.8 m × 1.8 m × 2.1 m localization area. In order to verify the adaptability of the MMBP algorithm, we experimentally demonstrate two different training data acquisition methods adopting either even or arbitrary training sets. In addition, we also demonstrate the positioning accuracy of the traditional RSS algorithm. Experimental results show that the average localization accuracy optimized by our proposed algorithm is only 1.88 cm for the arbitrary set and 1.99 cm for the even set, while the average positioning error of the traditional RSS algorithm reaches 14.34 cm. Comparison indicates that the positioning accuracy of our proposed algorithm is 7.6 times higher. Results also show that the performance of our system is higher than some previous reports based on RSS and RSS fingerprint databases using complex machine learning algorithms trained by a large amount of training points.

This paper studied the mechanism of a multi-wheel intelligent-surmounting obstacle vehicle and its principal controlling technical routine with single chip CPU. The model vehicle mounted many wheels with gear transmission; the controlling system use Freescale 16 bites MC9S128 single chip CPU as the core controlling unit, some touching switches used as obstacle detection sensor. By effective signal treatment, the basic function of the vehicle, such as steering engine turning, rod lifting, wheel backward retraction and return after over obstacle is performed correspondingly. The experiment shows that this model vehicle can realize automatic surmounting obstacle without moving direction change through multi-wheel coordination.