This paper proposes Kalman-filter drift removal (DR) and Heron-bilateration location estimation (LE) to significantly reduce the received signal strength index (RSSI) drift, localization error, computational complexity, and deployment cost of conventional radio frequency identification (RFID) indoor positioning systems without any sacrifice of localization granularity and accuracy. By means of only one portable RFID reader as the targeted device and only one pair of active RFID tags as the border-deployed landmarks, this paper develops a real-time portable RFID indoor positioning device and costeffective scalable RFID indoor positioning infrastructure, based on Kalman-filter DR, Heron-bilateration LE, and four novel preprocessing/postprocessing techniques. Experimental results reveal that the proposed Kalman-filter DR method is faster and better to converge the distance measurement (DM) error than conventional probability/statistics in terms of various relative distances under certain RSSI drift effect condition, and the proposed Heron-bilateration LE method is also faster and better to converge the LE error than conventional proximity pattern matching and trilateration in terms of three or more landmarks under certain DM error condition. On the other hand, a portable RFID indoor positioning device is smoothly implemented on an Android smartphone platform attached with a portable Bluetooth-based RFID reader.Index Terms-Drift removal (DR), indoor positioning, location estimation (LE), radio frequency identification (RFID).

No abstract

No abstract

No abstract

This paper proposes a simple but practical 2D ear detection algorithm based on arc-masking candidate extraction and AdaBoost polling verification. In the first half phase of the proposed ear detection algorithm, a few ear candidates are extracted by arc-masking edge search followed by multilayer mosaic and orthogonal projection histogram. Then, in the second half phase, the most likely ear candidate is picked out by rough AdaBoost polling verification. Experimental results show that the proposed ear detection algorithm can achieve a bit higher detection hit rate and much lower detection false alarm rate than conventional AdaBoost ear detection algorithm with Haar-like features under various pose rotation conditions.