A miniaturized Co-based amorphous wire GMI (Giant magneto-impedance) magnetic sensor was designed and fabricated in this paper. The Co-based amorphous wire was used as the sense element due to its high sensitivity to the magnetic field. A three-dimensional micro coil surrounding the Co-based amorphous wire was fabricated by MEMS (Micro-Electro-Mechanical System) technology, which was used to extract the electrical signal. The three-dimensional micro pick-up coil was designed and simulated with HFSS (High Frequency Structure Simulator) software to determine the key parameters. Surface micro machining MEMS (Micro-Electro-Mechanical System) technology was employed to fabricate the three-dimensional coil. The size of the developed amorphous wire magnetic sensor is 5.6 × 1.5 × 1.1 mm3. Helmholtz coil was used to characterize the performance of the device. The test results of the sensor sample show that the voltage change is 130 mV/Oe and the linearity error is 4.83% in the range of 0~45,000 nT. The results indicate that the developed miniaturized magnetic sensor has high sensitivity. By testing the electrical resistance of the samples, the results also showed high uniformity of each device.
Indoor localization and navigation have a great potential of application, especially in large indoor spaces where people tend to get lost. The indoor localization problem is the fundamental of an indoor navigation system. Existing research and commercial efforts have leveraged wireless-based approaches to locate users in indoor environments. However, the predominant wireless-based approaches, such as WiFi and Bluetooth, are still not satisfactory, either not supporting commodity devices, or being vulnerable to environmental changes. These issues make them hard to deploy and maintain. In this paper, we present Vivid, a mobile device-friendly indoor localization and navigation system that leverages visual cues as the cornerstone of localization. By leveraging the computation power at the extreme internet edges, Vivid to a large extent overcomes the difficulties brought by resource-intensive image processing tasks. We propose a grid-based algorithm that transforms the feature map into a grid, with which finding the path between two positions can be easily obtained. We also leverage deep learning techniques to assist in automatic map maintenance to adapt to the visual changes and make the system more robust. With edge computing, user privacy is preserved since the visual data is mainly processed locally and detected dynamic objects are removed immediately without saving to databases. The evaluation results show that: i) our system easily outperforms the existing solutions on COTS devices in localization accuracy, yielding decimeter-level error; ii) our choice of the system architecture is scalable and optimal among the available ones; iii) the automatic map maintenance mechanism effectively ameliorates the localization robustness of the system.INDEX TERMS Last mile delivery, loTs-based indoor localization and navigation, edge computing for loTs sensors.
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