Quantum gates which are represented by unitary matrices have potentials to implement the reversible logic circuits. M and M+ gates are two well-known quantum gates which are used to synthesize the reversible logic circuits. In this work, we have used behavioral description of these gates, instead of unitary matrix description, to synthesize reversible logic circuits. By this method, M and M+ gates are shown in the truth table form.

In this paper, a random transistor-transistor logic signal generator and a synchronization circuit are designed and implemented in lab-scale measurement device independent–quantum key distribution systems. The random operation of the weak coherent sources and the system’s synchronization signals were tested by a time to digital convertor.

In this work a model of a source generating truly random quadrature phase shift keying (QPSK) signal constellation required for quantum key distribution (QKD) system based on BB84 protocol using phase coding is implemented by using the software package OPTISYSTEM9. The randomness of the sequence generated is achieved by building an optical setup based on a weak laser source, beam splitters and single-photon avalanche photodiodes operating in Geiger mode. The random string obtained from the optical setup is used to generate the quadrature phase shift keying signal constellation required for phase coding in quantum key distribution system based on BB84 protocol with a bit rate of 2GHz/s.

   This paper determined the difference between the first image of the natural and the second infected image by using logic gates. The proposed algorithm was applied in the first time with binary image, the second time in the gray image, and in the third time in the color image. At start of proposed algorithm the process  images by applying convolution to extended images with zero to obtain more vision and features then enhancements images by Edge detection filter (laplacion operator) and smoothing images by using mean filter ,In order to determine the change between the original image and the injury the logic gates applied specially X-OR gates . Applying the technique for tooth decay through this comparison can locate inj

Electrocardiogram (ECG) is an important physiological signal for cardiac disease diagnosis. With the increasing use of modern electrocardiogram monitoring devices that generate vast amount of data requiring huge storage capacity. In order to decrease storage costs or make ECG signals suitable and ready for transmission through common communication channels, the ECG data
volume must be reduced. So an effective data compression method is required. This paper presents an efficient technique for the compression of ECG signals. In this technique, different transforms have been used to compress the ECG signals. At first, a 1-D ECG data was segmented and aligned to a 2-D data array, then 2-D mixed transform was implemented to compress the

  The main aim of this research to study and recognize the specifications and main concepts of  (Fuzzy Logic) and its components and studying the practical experiments  of the (Fuzzy Logic)  techniques in the electrical engineering field through by using the (Fuzzy Logic) for controlling the three-phase AC induction motor by using (Matlab_ simulation_7) for modeling the system by using the computer

This paper shows an approach for Electromyography (ECG) signal processing based on linear and nonlinear adaptive filtering using Recursive Least Square (RLS) algorithm to remove two kinds of noise that affected the ECG signal. These are the High Frequency Noise (HFN) and Low Frequency Noise (LFN). Simulation is performed in Matlab. The ECG, HFN and LFN signals used in this study were downloaded from ftp://ftp.ieee.org/uploads/press/rangayyan/, and then the filtering process was obtained by using adaptive finite impulse response (FIR) that illustrated better results than infinite impulse response (IIR) filters did.

A new algorithm is proposed to compress speech signals using wavelet transform and linear predictive coding. Signal compression based on the concept of selecting a small number of approximation coefficients after they are compressed by the wavelet decomposition (Haar and db4) at a suitable chosen level and ignored details coefficients, and then approximation coefficients are windowed by a rectangular window and fed to the linear predictor. Levinson Durbin algorithm is used to compute LP coefficients, reflection coefficients and predictor error. The compress files contain LP coefficients and previous sample. These files are very small in size compared to the size of the original signals. Compression ratio is calculated from the size of th