A common application of digital watermarking is to encode a small packet of information in an image, such as some form of identification that can be represented as a bit string. One class of digital watermarking techniques employs spread spectrum like methods where each bit is redundantly encoded throughout the image in order to mitigate bit errors. We typically require that all bits be recover...

Nurses spend much more time dealing with patients compared with other healthcare personnel. This group has a significant role in identification of threatening complexities of patients, removing medical errors and in keeping patients safe, on the other hand, a considerable amount of human errors is committed by nurses (1). Therefore, nurses have to face with many occupational risks compared with...

o enhance the performances of rough-neural networks (R-NNs) in the system identification‎, ‎on the base of emotional learning‎, ‎a new stable learning algorithm is developed for them‎. ‎This algorithm facilitates the error convergence by increasing the memory depth of R-NNs‎. ‎To this end‎, ‎an emotional signal as a linear combination of identification error and its differences is used to achie...

A 27 kt water volume is investigated as a target for a long baseline neutrino beam from CERN to Gran Sasso. Charged secondaries from the neutrtno interactions produce Cherenkov photons in water which are imaged as rings by a spherical mirror. The photon detector elements are 14 400 photomultipliers (PM’s) of 127 mm diameter or 3600 HPlYs of 250 mm diameter with single photon sensitivity. A coin...

This study presents a new procedure based on Artificial Neural Network (ANN) for identification of double cracks in Functionally Graded Beams (FGBs). A cantilever beam is modeled using Finite Element Method (FEM) for analyzing a double-cracked FGB and evaluation of its first four natural frequencies for different cracks depths and locations. The obtained FEM results are verified against availab...

This paper investigates the ability of the stochastic subspace identification technique to return a valid model from finite measurement data, its asymptotic properties as the data set becomes large, and asymptotic error bounds of the identified model (in terms of H2 and H∞ norms). First, a new and straightforward LMI-based approach is proposed, which returns a valid identified model even in cas...