Different from [23], Shi et al. [8] exploits the distinct RSS variation behaviors between an on-body and an off-body communication channel to distinguish legitimate nodes from false ones. Nevertheless, Shi et al. [8] is not suitable for the crowded scenario, and it assumes that attackers’ directional antenna cannot be directed towards the user. The authors of [24] propose a device paring scheme using different RSS to perform proximity detection.Proximity-based authentication: Authentication schemes can be based on proximity detection. In many circumstances, the adversary cannot come close to the user’s devices or cannot do so without being detected. This idea originates from [25]. Under the inspiration of [25,26] utilizes radio frequency (RF) and ultrasound to determine a device’s proximity for controlling IMDs’ access. Normally, it needs specialized hardware for high accuracy. In [27], RF distance bounding that fully uses the wireless channel is first designed, but multi-radio capabilities and additional hardware are needed. Some channel-based authentication schemes, such as [8,21,22,24], are also based on proximity. Obviously, the adversary cannot get close to the user without being detected in BAN. Additionally, the first lightweight BAN authentication scheme [8] is an example.Motivat
Video-based methods have recently been introduced for a variety of applications in structural health monitoring (SHM). Patsias and Staszewski [1] analyzed digital videos for edge detection and to approximate the mode shape of a cantilever in a laboratory experiment. By applying a wavelet transform to the mode shape they were able to detect the location of damage which was introduced by cutting a groove with increasing depth into the cross-section. Lee et al. [2] devised a real-time method to measure in-plane displacements and rotations using feature tracking techniques based on a Lagrangian approach, and applied it to a target bridge. Zaurin and Catbas [3�C7] developed a method using digital video data to locate and measure applied loads on a bridge and devised an index called unit influence line (UIL) as a measure of the health of bridges. www.selleckchem.com/products/Calcitriol-(Rocaltrol).html Elgamal et al. [8] developed a framework to integrate different data types including computer vision data to create a ��decision-support system�� for bridges and other lifelines. In a SHM review on wind turbines by Ciang et al. [9], it is noted that digital image correlation (DIC) techniques can also be used for these structures, but the 3-D version of these methods should be investigated in more depth if they are to be applied. Song et al. [10] modified the Hough Transform to track numerous markers on a beam with a computationally efficient algorithm and fitted a spline curve to the tracked shape in order to detect the location of the damage.To conclude, the use of digital videos for SHM is only in the beginning stage.