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.

With the rapid development of smart technology, the need for location-based services (LBS) increases every day. Since classical positioning technology such as GPS cannot satisfy the needs of indoor positioning, new indoor positioning technologies, such as Bluetooth, Wi-Fi, and Visible light communication (VLC), have already cut a figure. VLC positioning has been proposed because it has higher accuracy, costs less, and is easier to accomplish in comparison to the other indoor positioning technologies. However, the practicality of VLC positioning is limited since it is easily affected by multipath effects and the layout of LEDs. Thus, we propose a fusion positioning system based on extended Kalman filters, which can fuse the VLC position and the inertial navigation data. The accuracy of the fusion positioning system is in centimeters, which is better compared to the VLC-based positioning or inertial navigation alone. Furthermore, the fusion positioning system has high accuracy, saves energy, costs little, and is easy to install, making it a promising candidate for future indoor positioning applications.

Two formaldehyde detection methods are proposed by applying composite film quartz crystal microbalance (QCM) sensors. QCM sensor coated with PAAm/MWCNTs and PVAm/MWCNTs shows excellent characteristics of lower limit and high sensitivity. The lower limit of PVAm/MWCNTs is 0.5 ppm, and its detection sensitivity is 0.74 ppm/Hz. Upon working at different concentrations of formaldehyde and fabricating in different proportions, the reuse performance, gas selectivity, and response at room temperature show contrasting results. The main advantages of the two sensors presented are fast reaction, low cost, and easy manufacture. Compared to other formaldehyde sensors based on QCM, the PAAm/MWCNT- and PVAm/MWCNT-coated QCM sensors are able to concurrently show excellent selectivity, reuse performance, and high sensitivity, which is of great significance to detect the environmental quality.

Indoor visible light positioning becomes attractive due to the increasing demands of location-based services. This Letter proposes an indoor imaging visible light positioning scheme with a sampled sparse light source, image sensor, and gyro. An indoor positioning cellular with a single reference light source and an off-the-shelf mobile device is demonstrated. Experimental results show that the 3-dimensional positioning error is only several centimeters even with a rotated, rolled, and pitched mobile device. The proposed scheme is convenient and cost effective because the transmitter takes advantage of the existing lighting infrastructure and the receiver is a commercial mobile phone without any extra accessories.

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.