This study presents an integrated Artificial Neural Network (ANN) and time series framework to estimate and predict Signal to Interference Ratio (SIR) in Direct Sequence Code Division Multiple Access (DS/CDMA) systems. It is difficult to model uncertain behavior of SIR with only conventional ANN or time series and the integrated algorithm could be an ideal substitute for such cases. Artificial ...

A prototype multi-stream system with a performance monitor for stream selection is proposed to recognize speech in unknown noise. The speech signal is decomposed into seven band-limited streams. Posterior probabilities of phonemes are estimated by a multi-layer perceptron (MLP) in each of these band-limited streams. Estimated posterior vectors of all 127 combinations (processing streams) of the...

In now a days Password authentication plays a vital role typing biometrics as a transparent layer of user authentication for security. Keystroke authentication provides security to the applications like electronic mail, banking and any other web services etc., There are several types of techniques to protect our passwords but it can be easily hacked by the hackers through so many algorithms. By...

Artificial Neural Network (ANN) is an effective technique of Soft Computing can model ComputerAided Diagnosis (CAD) system efficiently. CAD system is an essential for the prediction of Malignancy in Cervical Cancer. Cervical Cancer can be cured if it is diagnosed in early stages. Hence, for the effective screening of cancer lesions in the Cervical cell images which are captured using Pap smear ...

Most of the current SAR systems aquire fully polarimetric data where the obtained scattering information can be represented by various coherent and incoherent parameters. In previous contributions we reviewed these parameters in terms of their “utility” for landcover classification, here, we investigate their impact on several classification algoritms. Three classifiers: the minimum-distance cl...

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...

The majority of today's Neural Networks are either Multi-Layer-Perceptron networks (MLP) or Feature-Maps like Kohonen's Self-Organizing Map (SOM). Usually they are simulated on ordinary single-processor von-Neumann hardware to be used for some kind of vector quantization, classiication or coding. However, these simulated Neural Networks are expensive in respect to the computational costs they d...

Localization with noisy distance measurements is a critical problem in many applications of wireless sensor networks. Different localization algorithms offer different tradeoffs between accuracy and hardware resource requirements. In order to provide insight into selecting the best algorithm that optimizes this tradeoff, this paper evaluates the accuracy, memory, and computational requirements ...

this research intends to develop a method based on the artificial neural network (ann) to predict permanent earthquake-induced deformation of the earth dams and embankments. for this purpose, data sets of observations from 152 published case histories on the performance of the earth dams and embankments, during the past earthquakes, was used. in order to predict earthquake-induced deformation o...