In this paper, a vibration-based MEMS electromagnetic energy harvester (EM-EH) device with two-degree-of-freedom (2DOF) configuration has been presented, modeled and characterized. The proposed 2DOF system comprises a primary subsystem for power generation, and an accessory subsystem for frequency tuning. A lumped parametric 2DOF model is built and examined in respect of energy harvesting capabilities. By controlling the mass ratio and frequency ratio, the first two resonances of primary mass can be tuned close to each other while maintaining comparable magnitudes. The 2DOF configuration is expected to be more adaptive and efficient than the conventional 1DOF structure, which could only operate near its sole resonance. The 2DOF EM-EH chip is fabricated on silicon-on-insulator (SOI) wafer through double-sided deep reactive-ion etching (DRIE). Induction coil is only patterned on the primary mass for energy conversion. With current prototype at an acceleration of 0.12 g, two resonances of 326 and 391 Hz with output voltages of 3.6 and 6.5 mV are obtained respectively, providing good validation for the modeling results. This paper offers new insights of implementing a multimodal MEMS EM-EH device.

There is a growing demand for cheap and fast cadastral mapping methods to face the challenge of 70% global unregistered land rights. As traditional on-site field surveying is time-consuming and labor intensive, imagery-based cadastral mapping has in recent years been advocated by fit-for-purpose (FFP) land administration. However, owing to the semantic gap between the high-level cadastral boundary concept and low-level visual cues in the imagery, improving the accuracy of automatic boundary delineation remains a major challenge. In this research, we use imageries acquired by Unmanned Aerial Vehicles (UAV) to explore the potential of deep Fully Convolutional Networks (FCNs) for cadastral boundary detection in urban and semi-urban areas. We test the performance of FCNs against other state-of-the-art techniques, including Multi-Resolution Segmentation (MRS) and Globalized Probability of Boundary (gPb) in two case study sites in Rwanda. Experimental results show that FCNs outperformed MRS and gPb in both study areas and achieved an average accuracy of 0.79 in precision, 0.37 in recall and 0.50 in F-score. In conclusion, FCNs are able to effectively extract cadastral boundaries, especially when a large proportion of cadastral boundaries are visible. This automated method could minimize manual digitization and reduce field work, thus facilitating the current cadastral mapping and updating practices.

Big data-based acquisition and storage system (ASS) plays an important role in the design of industrial data platform. Many big data frameworks have been integrated compression and serialization method. These methods cannot meet the needs of industrial production information management for requiring time-consuming and mass storage. Based on the existing big data frameworks, we propose an enhanced industrial big data platform in order to reduce the data processing time while requiring fewer data storage space. Specifically, this paper focuses on evaluating the impact of multiple compression and serialization methods on big data platform performance and tries to choose optimal compression and serialization methods for the industrial data platform. Compared to the methods integrated in Hadoop and Spark, the experimental results showed the data compression time of the platform has been reduced by 73.9% with a less than 96% the size of data compressed, furthermore, the data serialization time has been reduced by 80.8%. With the increasing amount of data, it takes less time to compare with benchmark methods.

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