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We present WiGest: a system that leverages changes in WiFi signal strength to sense in-air hand gestures around the user's mobile device. Compared to related work, WiGest is unique in using standard WiFi equipment, with no modifications, and no training for gesture recognition. The system identifies different signal change primitives, from which we construct mutually independent gesture families. These families can be mapped to distinguishable application actions. We address various challenges including cleaning the noisy signals, gesture type and attributes detection, reducing false positives due to interfering humans, and adapting to changing signal polarity. We implement a proof-of-concept prototype using off-the-shelf laptops and extensively evaluate the system in both an office environment and a typical apartment with standard WiFi access points. Our results show that WiGest detects the basic primitives with an accuracy of 87.5% using a single AP only, including through-the-wall non-line-of-sight scenarios. This accuracy increases to 96% using three overheard APs. In addition, when evaluating the system using a multi-media player application, we achieve a classification accuracy of 96%. This accuracy is robust to the presence of other interfering humans, highlighting WiGest's ability to enable future ubiquitous hands-free gesturebased interaction with mobile devices.

No abstract

Device-free (DF) indoor localization has grasped great attention recently as a value-added service to the already installed WiFi infrastructure as it allows the tracking of entities that do not carry any devices nor participate actively in the localization process. Current approaches, however, require a relatively large number of wireless streams, i.e. transmitterreceiver pairs, which is not available in many typical scenarios, such as home monitoring.In this paper, we introduce MonoPHY as an accurate monostream device-free WLAN localization system. MonoPHY leverages the physical layer information of WiFi networks supported by the IEEE 802.11n standard to provide accurate DF localization with only one stream. In particular, MonoPHY leverages both the low-level Channel State Information and the MIMO information to capture the human effect on signal strength. Experimental evaluation in a typical apartment, with a side-by-side comparison with the state-of-the-art, shows that MonoPHY can achieve an accuracy of 1.36m. This corresponds to at least 48% enhancement in median distance error over the state-of-the-art DF localization systems using a single stream only.Index Terms-Device-free localization, detection and tracking, physical-layer based localization.

Recently, Question Answering (QA) has been one of the main focus of natural language processing research. However, Arabic Question Answering is still not in the mainstream. The challenges of the Arabic language and the lack of resources have made it difficult to provide Arabic QA systems with high accuracy. While low accuracies may be accepted for general purpose systems, it is critical in some fields such as religious affairs. Therefore, there is a need for specialized accurate systems that target these critical fields. In this paper, we propose Al-Bayan, a new Arabic QA system specialized for the Holy Quran. The system accepts an Arabic question about the Quran, retrieves the most relevant Quran verses, then extracts the passage that contains the answer from the Quran and its interpretation books (Tafseer). Evaluation results on a collected dataset show that the overall system can achieve 85% accuracy using the top-3 results.