In the indoor location field, the quality of received-signal-strength-indicator (RSSI) fingerprints plays a key role in the performance of indoor location services. However, changes in an indoor environment may lead to the decline of location accuracy. This paper presents a localization method employing a Hybrid Wireless fingerprint (HW-fingerprint) based on a convolutional neural network (CNN). In the proposed scheme, the Ratio fingerprint was constructed by calculating the ratio of different RSSIs from important contribution access points (APs). The HW-fingerprint combined the Ratio fingerprint and the RSSI to enhance the expression of indoor environment characteristics. Moreover, a CNN architecture was constructed to learn important features from the complex HW-fingerprint for indoor locations. In the experiment, the HW-fingerprint was tested in an actual indoor scene for 15 days. Results showed that the average daily location accuracy of the K-Nearest Neighbor (KNN), Support Vector Machines (SVMs), and CNN was improved by 3.39%, 8.03% and 9.03%, respectively, when using the HW-fingerprint. In addition, the deep-learning method was 4.19% and 16.37% higher than SVM and KNN in average daily location accuracy, respectively.

The radical suppression of the photodarkening effect and laser performance deterioration via H 2 loading were demonstrated in high-power Yb-doped fiber (YDF) amplifiers. The photodarkening loss at equilibrium was 114.4 dB/m at 702 nm in the pristine fiber, while it vanished in the H 2 -loaded fiber. To obtain a deeper understanding of the impact of photodarkening on laser properties, the evolution of the mode instability threshold and output power in fiber amplifiers was investigated. After pumping for 300 min, the mode instability threshold of the pristine fiber dropped from 770 to 612 W, and the periodic fluctuation of the output power became intense, finally reaching 100 W. To address the detrimental effects originating from photodarkening, H 2 loading was applied in contrast experiments. The output power remained stable, and no sign of mode instability was observed in the H 2 -loaded fiber. Moreover, the transmittance at 638 nm confirmed the absence of the photodarkening effect. The results pave the way for the further development of high-power fiber lasers.

It is often desirable to selectively remove corrupting or uninteresting signals from complex NMR spectra without disturbing overlapping or nearby signals. For biofluids in particular, removal of solvent and urea signals is important for retaining quantitative accuracy in NMR-based metabonomics. This article presents a novel algorithm for efficient filtering of unwanted signals using the filter diagonalization method (FDM). Unwanted signals are modeled in the time domain using FDM. This modeled signal is subtracted from the original free induction decay. The resulting corrected signal is then processed using established workflow. The algorithm is found to be reliable and fast. By eliminating large, broad, uninteresting signals, many spectra can be subjected to fully automated absolute value processing, allowing objective preparation of spectra for pattern recognition analysis.

MFC was a viable alternative to conventional spectrometry with the potential to be more simply implemented and more rapid and accurate.