This paper addresses reliable and accurate indoor localization using inertial sensors commonly found on commodity smartphones. We believe indoor positioning is an important primitive that can enable many ubiquitous computing applications. To tackle the challenges of drifting in estimation, sensitivity to phone position, as well as variability in user walking profiles, we have developed algorithms for reliable detection of steps and heading directions, and accurate estimation and personalization of step length. We've built an end-to-end localization system integrating these modules and an indoor floor map, without the need for infrastructure assistance. We demonstrated for the first time a meterlevel indoor positioning system that is infrastructure free, phone position independent, user adaptive, and easy to deploy. We have conducted extensive experiments on users with smartphone devices, with over 50 subjects walking over an aggregate distance of over 40 kilometers. Evaluation results showed our system can achieve a mean accuracy of 1.5m for the in-hand case and 2m for the in-pocket case in a 31m×15m testing area.

Anomalies of the omnipresent earth magnetic (i.e., geomagnetic) field in an indoor environment, caused by local disturbances due to construction materials, give rise to noisy direction sensing that hinders any dead reckoning system. In this paper, we turn this unpalatable phenomenon into a favorable one. We present Magicol, an indoor localization and tracking system that embraces the local disturbances of the geomagnetic field. We tackle the low discernibility of the magnetic field by vectorizing consecutive magnetic signals on a per-step basis, and use vectors to shape the particle distribution in the estimation process. Magicol can also incorporate WiFi signals to achieve much improved positioning accuracy for indoor environments with WiFi infrastructure. We perform an in-depth study on the fusion of magnetic and WiFi signals. We design a two-pass, bidirectional particle filtering process for maximum accuracy, and propose an on-demand WiFi scan strategy for energy savings. We further propose a compliant-walking method for location database construction that drastically simplifies the site survey effort. We conduct extensive experiments at representative indoor environments, including an office building, an underground parking garage, and a supermarket in which Magicol achieved a 90 percentile localization accuracy of 5m, 1m, and 8m, respectively, using the magnetic field alone. The fusion with WiFi leads to 90 percentile accuracy of 3.5m for localization and 0.9m for tracking in the office environment. When using only the magnetism, Magicol consumes 9× less energy in tracking compared to WiFibased tracking.

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Refined coal pitch is a recognized precursor to produce high-quality needle coke with its higher aromaticity, lower ash content, and a relatively narrow distribution of molecular weight. The aromatic index (f a) of refined coal pitch is one of the key roles in the production of high-quality needle coke. In order to a detailed study on the effects of f a on the microstructure and properties of needle coke, 9 kinds of refined coal pitches with varied f a were used as the raw materials to produce needle coke in this study. Briefly, 1H NMR was used to calculate the f a of each refined coal pitch. Polarizing microscope, scanning electron microscopy, microstrength tester, X-ray diffraction, Raman spectrum, and curve-fitted methods have been used to quantitatively examine the microstructure and microstrength of each needle coke. The results have shown that the refined coal pitch with the f a of 0.95–0.98 was the best precursor to produce high-quality needle coke.

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