With the ubiquitous deployment of wireless systems and pervasive availability of smart devices, indoor localization is empowering numerous location-based services. With the established radio maps, WiFi fingerprinting has become one of the most practical approaches to localize mobile users. However, most fingerprint-based localization algorithms are computation-intensive, with heavy dependence on both offline training phase and online localization phase. In this paper, we propose CNNLoc, a Convolutional Neural Network (CNN) based indoor localization system with WiFi fingerprints for multi-building and multi-floor localization. Specifically, we devise a novel classification model and a novel positioning model by combining a Stacked Auto-Encoder (SAE) with a one-dimensional CNN. The SAE is utilized to precisely extract key features from sparse Received Signal Strength (RSS) data while the CNN is trained to effectively achieve high accuracy in the positioning phase. We evaluate the proposed system on the UJIIndoorLoc dataset and Tampere dataset and compare the performance with several state-of-the-art methods. Moreover, we further propose a newly collected WiFi fingerprinting dataset UTSIndoorLoc and test the positioning model of CNNLoc on it. The results show CNNLoc outperforms the existing solutions with 100% and 95% success rates on building-level localization and floor-level localization, respectively.

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A two-phase flow CFD model using the volume of fluid (VOF) method is presented for predicting the hydrodynamics of falling film flow on inclined plates, corresponding to the surface texture of structured packing. Using the proposed CFD model the influence of the solid surface microstructure, liquid properties and gas flow rate on the flow behavior was investigated. From the simulated results it was shown that under the condition of no gas flow the liquid flow patterns are dependent on the microstructure of the plates, and proper microstructuring of the solid surface will improve the formation of a continuous liquid film. It was also found that liquid properties, especially surface tension, play an important role in determining the thinfilm pattern. However, there are very different liquid film patterns under the action of gas flow. Thinner liquid films break easily, but thicker liquid films can remain continuous even at higher gas flow rates, which demonstrates that all factors affecting the liquid film thickness will affect the liquid film patterns under conditions of counter-current two-phase flow.