In this paper different channel coding and interleaving schemes in DS/CDMA system over multipath fading channel were used. Two types of serially concatenated coding were presented. The first one composed of Reed-Solomon as outer code, convolutional code as inner code and the interleaver between the outer and inner codes and the second consist of convolutional code as outer code, interleaved in the middle and differential code as an inner code. Bit error rate performance of different schemes in multipath fading channel was analyzed and compared. Rack receiver was used in DS/CDMA receiver to combine multipath components in order to enhance the signal to noise ratio at the receiver.

In this study, a traumatic spinal cord injury (TSCI) classification system is proposed using a convolutional neural network (CNN) technique with automatically learned features from electromyography (EMG) signals for a non-human primate (NHP) model. A comparison between the proposed classification system and a classical classification method (k-nearest neighbors, kNN) is also presented. Developing such an NHP model with a suitable assessment tool (i.e., classifier) is a crucial step in detecting the effect of TSCI using EMG, which is expected to be essential in the evaluation of the efficacy of new TSCI treatments. Intramuscular EMG data were collected from an agonist/antagonist tail muscle pair for the pre- and post-spinal cord lesi

The Evaluation of the immune response in Golden Hamsters experimentally infected with Leishmania donovani was determined in this study, particularly, the cellular immune response. Follow up has maintained to determine the Delayed Type of Hypersensitivity using skin test both in infected and control lab animals. Chicken red blood cells were used as a parameter to evaluate the immune system; they are dull and have the ability of immunization. Two concentrations of chicken R.B.C were examined to determine which gives the higher titration in Hamsters and those were 1.5 X 109 cell/ml and 3 X 109 cell/ml , the second concentration gave the maximum titration where then used in this work. After sensitization with Chicken R.B.C for both in

The Evaluation of the immune response in Golden Hamsters experimentally infected with Leishmania donovani was determined in this study, particularly, the cellular immune response. Follow up has maintained to determine the Delayed Type of Hypersensitivity using skin test both in infected and control lab animals. Chicken red blood cells were used as a parameter to evaluate the immune system; they are dull and have the ability of immunization. Two concentrations of chicken R.B.C were examined to determine which gives the higher titration in Hamsters and those were 1.5 X 109 cell/ml and 3 X 109 cell/ml , the second concentration gave the maximum titration where then used in this work. After sensitization with Chicken R.B.C for both infected a

The study using Nonparametric methods for roubust to estimate a location and scatter it is depending  minimum covariance determinant of multivariate regression model , due to the presence of outliear values and increase the sample size and presence of more than after the model regression multivariate therefore be difficult to find a median location .       

It has been the use of genetic algorithm Fast – MCD – Nested Extension and compared with neural Network Back Propagation of multilayer in terms of accuracy of the results and speed in finding median location ,while the best sample to be determined by relying on less distance (Mahalanobis distance)has the stu

Human identification is crucial in forensics for the investigation of large-scale disasters such as fires, epidemics, earthquakes, and tsunamis. Even though biometric identification using panoramic dental radiography (PDR) has been the subject of several studies in the literature, further study remains a necessary and challenging issue. In this research, a human identification system was developed based on a convolutional neural network (CNN) and contour transform (CT). The proposed system was implemented on a total of 1540 PDR from 302 individuals. The preprocessing applied to PDRs for enhancing and taking the Region of Interest (ROI). The features were extracted using CT transform. These features were fused with features extracted

The artificial intelligence techniques such as neural networks and fuzzy systems play an important role to disconnect flexion & expansion of the swing leg, the earth response force of the other foot has been redesigned. Under that paper, we think the fuzzy controller plan issue for yield following flawed genuine investigation of nonlinear systems. For examination, an essential fuzzy control plot has been bristly developed dependent on a current methodology delegate under the field. In this paper, the Feedforward Neural Network has been implemented with integer, fixed point and floating point data representations. Additionally, The Fuzzy Logic Controllers in both analog and digital forms has been implemented in hardware. Both designs use les

In this article, we design an optimal neural network based on new LM training algorithm. The traditional algorithm of LM required high memory, storage and computational overhead because of it required the updated of Hessian approximations in each iteration. The suggested design implemented to converts the original problem into a minimization problem using feed forward type to solve non-linear 3D - PDEs. Also, optimal design is obtained by computing the parameters of learning with highly precise. Examples are provided to portray the efficiency and applicability of this technique. Comparisons with other designs are also conducted to demonstrate the accuracy of the proposed design.

In this paper, an algorithm is suggested to train a single layer feedforward neural network to function as a heteroassociative memory. This algorithm enhances the ability of the memory to recall the stored patterns when partially described noisy inputs patterns are presented. The algorithm relies on adapting the standard delta rule by introducing new terms, first order term and second order term to it. Results show that the heteroassociative neural network trained with this algorithm perfectly recalls the desired stored pattern when 1.6% and 3.2% special partially described noisy inputs patterns are presented.