**Commenced**in January 2007

**Frequency:**Monthly

**Edition:**International

**Paper Count:**5

# Publications

##### 5 A Fast Neural Algorithm for Serial Code Detection in a Stream of Sequential Data

**Authors:**
Hazem M. El-Bakry,
Qiangfu Zhao

**Abstract:**

In recent years, fast neural networks for object/face detection have been introduced based on cross correlation in the frequency domain between the input matrix and the hidden weights of neural networks. In our previous papers [3,4], fast neural networks for certain code detection was introduced. It was proved in [10] that for fast neural networks to give the same correct results as conventional neural networks, both the weights of neural networks and the input matrix must be symmetric. This condition made those fast neural networks slower than conventional neural networks. Another symmetric form for the input matrix was introduced in [1-9] to speed up the operation of these fast neural networks. Here, corrections for the cross correlation equations (given in [13,15,16]) to compensate for the symmetry condition are presented. After these corrections, it is proved mathematically that the number of computation steps required for fast neural networks is less than that needed by classical neural networks. Furthermore, there is no need for converting the input data into symmetric form. Moreover, such new idea is applied to increase the speed of neural networks in case of processing complex values. Simulation results after these corrections using MATLAB confirm the theoretical computations.

**Keywords:**
Fast Code/Data Detection,
Neural Networks,
Cross Correlation,
real/complex values.

##### 4 Fast Complex Valued Time Delay Neural Networks

**Authors:**
Hazem M. El-Bakry,
Qiangfu Zhao

**Abstract:**

**Keywords:**
Fast Complex Valued Time Delay Neural
Networks,
Cross Correlation,
Frequency Domain

##### 3 Fast Object/Face Detection Using Neural Networks and Fast Fourier Transform

**Authors:**
Hazem M. El-Bakry,
Qiangfu Zhao

**Abstract:**

**Keywords:**
Conventional Neural Networks,
Fast Neural
Networks,
Cross Correlation in the Frequency Domain.

##### 2 A Modified Cross Correlation in the Frequency Domain for Fast Pattern Detection Using Neural Networks

**Authors:**
Hazem M. El-Bakry,
Qiangfu Zhao

**Abstract:**

**Keywords:**
Fast Pattern Detection,
Neural Networks,
Modified Cross Correlation

##### 1 Sub-Image Detection Using Fast Neural Processors and Image Decomposition

**Authors:**
Hazem M. El-Bakry,
Qiangfu Zhao

**Abstract:**

In this paper, an approach to reduce the computation steps required by fast neural networksfor the searching process is presented. The principle ofdivide and conquer strategy is applied through imagedecomposition. Each image is divided into small in sizesub-images and then each one is tested separately usinga fast neural network. The operation of fast neuralnetworks based on applying cross correlation in thefrequency domain between the input image and theweights of the hidden neurons. Compared toconventional and fast neural networks, experimentalresults show that a speed up ratio is achieved whenapplying this technique to locate human facesautomatically in cluttered scenes. Furthermore, fasterface detection is obtained by using parallel processingtechniques to test the resulting sub-images at the sametime using the same number of fast neural networks. Incontrast to using only fast neural networks, the speed upratio is increased with the size of the input image whenusing fast neural networks and image decomposition.

**Keywords:**
Fast Neural Networks,
2D-FFT,
CrossCorrelation,
Image decomposition,
Parallel Processing.