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This work presents a novel indoor positioning system that exploits a hybrid algorithm for future productivity 4.0. In the proposed system, LEDs are signal transmitters that are deployed on a ceiling. The target receiver is a smart sensor that receives the frequency, color temperature and luminance of the LEDs. First, the system determines the frequency of the LEDs to select a subspace to narrow down from a positioning area and to compute the position of the target. This work proposes a hybrid fingerprint that is based on RSSI values. RSSI signals are easily disturbed by refraction or diffraction. This research is based on line-of-sight light emission, which increases the accuracy of indoor positioning.

Technology related to the Internet of Things and Big Data is developing rapidly, favoring agricultural management and cultivation. External environmental parameters affect crop growth. Irrigation operations involve multi-conditional decisions. Artificial intelligence and image processing technology are used to control watering, process environmental data, and use the processed data are then used to train models to predict soil moisture. The results thus obtained are used to divide soil into that with high, normal, and low moistures. A camera commonly used to capture images of soil, which are imported into a convolutional neural network for feature extraction. Next, the category of soil is predicted following deep learning. The results of the two predictions are sent to a fuzzy control system and the soil moisture interval is defined based on the category of crop that is being planted in it. The rule base is constructed using the defined parameters, and determines the judgment rule from the agricultural professional, And external conditions lead to the fuzzy controller. Defuzzified results are imported into controlled a control device. This work uses the IoT to serialize information from the fields in a farm and uses artificial intelligence techniques to analyze the state of plants. Image recognition technology is to provide more precise and comprehensive control to improve the accuracy of irrigation.

Efforts have been made to elucidate the origin of d(0) magnetism in ZnO nanocactuses (NCs) and nanowires (NWs) using X-ray-based microscopic and spectroscopic techniques. The photoluminescence and O K-edge and Zn L3,2-edge X-ray-excited optical luminescence spectra showed that ZnO NCs contain more defects than NWs do and that in ZnO NCs, more defects are present at the O sites than at the Zn sites. Specifically, the results of O K-edge scanning transmission X-ray microscopy (STXM) and the corresponding X-ray-absorption near-edge structure (XANES) spectroscopy demonstrated that the impurity (non-stoichiometric) region in ZnO NCs contains a greater defect population than the thick region. The intensity of O K-edge STXM-XANES in the impurity region is more predominant in ZnO NCs than in NWs. The increase in the unoccupied (occupied) density of states at/above (at/below) the conduction-band minimum (valence-band maximum) or the Fermi level is related to the population of defects at the O sites, as revealed by comparing the ZnO NCs to the NWs. The results of O K-edge and Zn L3,2-edge X-ray magnetic circular dichroism demonstrated that the origin of magnetization is attributable to the O 2p orbitals rather than the Zn d orbitals. Further, the local density approximation (LDA) + U verified that vacancies in the form of dangling or unpaired 2p states (due to Zn vacancies) induced a significant local spin moment in the nearest-neighboring O atoms to the defect center, which was determined from the uneven local spin density by analyzing the partial density of states of O 2p in ZnO.