Ambiguity is a major reason why computers do not yet understand natural language. We have made great deal strides towards developing tools for morphological and syntactic analyzers for Arabic in recent years. The absence of diacritics, which represent most vowels, in the written text creates ambiguity which hinders the development of Arabic natural language processing applications. Thus, ambiguity increases the range of possible interpretations of natural language. In this paper, we give a road map of solutions to common ambiguity problems inherent in parsing of Arabic sentence.

Robots are increasingly used in numerous life applications. Therefore, humans are looking forward to create productive robots. Robot learning is the process of obtaining additional information to accomplish an objective configuration. Moreover, robot learning from demonstration is to guide the robot the way to perform a particular task derived from human directions. Traditionally, modeling the demonstrated data was applied on discrete data which would result in learning outcome distortions. So as to overcome such distortion, preprocessing of the raw data is necessary. In this paper, trajectory learning from demonstration scheme is proposed. In our proposed scheme, the raw data are initially preprocessed by employing the principal component analysis algorithm. We experimentally compare our proposed scheme with the most recent proposed schemes. It is found that the proposed scheme is capable of increasing the efficiency by minimizing the error in comparison to the other recent work with significant reduced computational cost.

In this paper, two BCH soft decoders are proposed suitable for high rate codes with medium to large word length. The proposed decoders provide a programmable performance gain, with a reduced critical path allowing for an increase upto m/2 times the operating frequency of algebraic decoders, where m is the Galois field size. Our proposed decoders operate only on the least reliable bits, which leads to a reduction in the decoder complexity by removing the Chien search procedure.

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The propagation in water of nanosecond focused laser pulses having power densities up to terra watts / cm2 and wavelength of 532±2nm, has been investigated extensively by our group. Fast imaging as well as time resolved photography of the nonlinear optical processes, revealed the formation of running spherical foci as well as luminous cavitation bubbles along the direction of the propagating beam. Associated mechanical effect of shock pressure was measured at the exit window as soon as the bubbles collapsed, using piezoelectric silicon gauge. The lifetime of the luminous cavitation bubbles and running spherical foci were found to vary between 160 to 50 ps. Their propagation velocities were estimated to be of the order of 106 m/s. The results are discussed and could be explained partially according to the Moving Focus Model, the self-phase Modulation Model and other associated processes. © 2005 American Institute of Physics.

In this paper we propose an approach for underdetermined
blind separation in the case of additive Gaussian white noise and pink
noise in addition to the most challenging case where the number of source
signals is unknown. In addition to that, the proposed approach is appli-
cable in the case of separating I +3 source signals from I mixtures with
an unknown number of source signals and the mixtures have additive two
kinds of noises. This situation is more challenging and also more suitable
to practical real world problems. Moreover, unlike to some traditional
approaches, the sparsity conditions are not imposed. Firstly, the number
of source signals is approximated and estimated using multiple source
detection, followed by an algorithm for estimating the mixing matrix
based on combining short time Fourier transform and rough-fuzzy clus-
tering. Then, the mixed signals are normalized and the source signals
are recovered using multi-layer modied Gradient descent Local Hier-
archical Alternating Least Squares Algorithm exploiting the number of
source signals estimated , and the mixing matrix obtained as an input
and initialized by multiplicative algorithm for matrix factorization based
on alpha divergence. The computer simulation results show that the pro-
posed approach can separate I + 3 source signals from I mixed signals,
and it has superior evaluation performance compared to some traditional
approaches in recent references.