A growing number of sensors on smart mobile devices has led to rapid development of various mobile applications using location-based or context-aware services. Typically, outdoor localization techniques have relied on GPS, or on cellular infrastructure support. While GPS gives high positioning accuracy, it can quickly deplete the battery on the device. On the other hand, base station based localization has low accuracy. In search of alternative techniques for outdoor localization, several approaches have explored the use of data gathered from other available sensors, like accelerometer, microphone, compass, and even daily patterns of usage, to identify unique signatures that can locate a device. Signatures, or fingerprints of an area, are hidden cues existing around a user's environment. However, under different operating scenarios, fingerprint-based localization techniques have variable performance in terms of accuracy, latency of detection, battery usage. The main contribution of this survey is to present a classification of existing fingerprintbased localization approaches which intelligently sense and match different clues from the environment for location identification. We describe how each fingerprinting technique works, followed by a review of the merits and demerits of the systems built based on these techniques. We conclude by identifying several improvements and application domain for fingerprinting based localization.

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Primary breast osteosarcoma is a rare malignant tumor, with only small series reported. We report the case of an 89-year-old woman presenting with a large calcified tumor of the left breast, associated with calcified lung metastases. After non-conclusive biopsy, osteosarcoma was proven at tumorectomy.