| 1. |
- Cheng, Kang, et al.
(författare)
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In-Air Gesture Interaction : Real Time Hand Posture Recognition Using Passive RFID Tags
- 2019
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Ingår i: IEEE Access. - : IEEE. - 2169-3536. ; 7, s. 94460-94472
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Tidskriftsartikel (refereegranskat)abstract
- In-air gesture interaction enables a natural communication between a man and a machine with its clear semantics and humane mode of operation. In this paper, we propose a real-time recognition system on multiple gestures in the air. It uses the commodity off-the-shelf (COTS) reader with three antennas to detect the radio frequency (RF) signals of the passive radio frequency identification (RFID) Tags attached to the fingers. The idea derives from the crucial insight that the sequential phase profile of the backscatter RF signals is a reliable and well-regulated indicator insinuating space-time situation of the tagged object, which presents a close interdependency with tag's movements and positions. The KL divergence is utilized to extract the dynamic gesture segment by confirming the endpoints of the data flow. To achieve the template matching and classification, we bring in the dynamic time warping (DTW) and k-nearest neighbors (KNN) for similarity scores calculation and appropriate gesture recognition. The experiment results show that the recognition rates for static and dynamic gestures can reach 85% and 90%, respectively. Moreover, it can maintain satisfying performance under different situations, such as diverse antenna-to-user distances and being hidden from view by nonconducting obstacles.
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| 2. |
- Qi, Lingtao, et al.
(författare)
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A Novel Shilling Attack Detection Model Based on Particle Filter and Gravitation
- 2019
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Ingår i: China Communications. - : China Inst Communications. - 1673-5447. ; 16:10, s. 112-132
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Tidskriftsartikel (refereegranskat)abstract
- With the rapid development of e-commerce, the security issues of collaborative filtering recommender systems have been widely investigated. Malicious users can benefit from injecting a great quantities of fake profiles into recommender systems to manipulate recommendation results. As one of the most important attack methods in recommender systems, the shilling attack has been paid considerable attention, especially to its model and the way to detect it. Among them, the loose version of Group Shilling Attack Generation Algorithm (GSAGen(l)) has outstanding performance. It can be immune to some PCC (Pearson Correlation Coefficient)-based detectors due to the nature of anti-Pearson correlation. In order to overcome the vulnerabilities caused by GSAGen(l), a gravitation-based detection model (GBDM) is presented, integrated with a sophisticated gravitational detector and a decider. And meanwhile two new basic attributes and a particle filter algorithm are used for tracking prediction. And then, whether an attack occurs can be judged according to the law of universal gravitation in decision-making. The detection performances of GBDM, HHT-SVM, UnRAP, AP-UnRAP Semi-SAD, SVM-TIA and PCA-P are compared and evaluated. And simulation results show the effectiveness and availability of GBDM.
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