In this work, the performance of the receiver in a quantum cryptography system based on BB84 protocol is scaled by calculating the Quantum Bit Error Rate (QBER) of the receiver. To apply this performance test, an optical setup was arranged and a circuit was designed and implemented to calculate the QBER. This electronic circuit is used to calculate the number of counts per second generated by the avalanche photodiodes set in the receiver. The calculated counts per second are used to calculate the QBER for the receiver that gives an indication for the performance of the receiver. Minimum QBER, 6%, was obtained with avalanche photodiode excess voltage equals to 2V and laser diode power of 3.16 nW at avalanche photodiode temperature of -10

Focused research aims to provide a framework cognitive analytical nature of real estate investments and how they evaluated in the light of the assessment tools of modern theory of real options, and the possibility to rely on that theory in the detection of the true value of projects, real estate investments that would maximize the value of the investment decision taken, and the analysis of those projects that arise in the real estate markets and environments is the organization, which she was to make sure cases and high-risk, compared with entrances techniques, discounted cash flow (net present value). Based on the assumption lies in the possibility of the application of the implic

   Let M is a Г-ring. In this paper the concept of orthogonal symmetric higher bi-derivations on semiprime Г-ring is presented and studied and the relations of two symmetric higher bi-derivations on Г-ring are introduced.

Image pattern classification is considered a significant step for image and video processing.Although various image pattern algorithms have been proposed so far that achieved adequate classification,achieving higher accuracy while reducing the computation time remains challenging to date. A robust imagepattern classification method is essential to obtain the desired accuracy. This method can be accuratelyclassify image blocks into plain, edge, and texture (PET) using an efficient feature extraction mechanism.Moreover, to date, most of the existing studies are focused on evaluating their methods based on specificorthogonal moments, which limits the understanding of their potential application to various DiscreteOrthogonal Moments (DOMs). The

A method for Approximated evaluation of linear functional differential equations is described. where a function approximation as a linear combination of a set of orthogonal basis functions which are chebyshev functions .The coefficients of the approximation are determined by (least square and Galerkin’s) methods. The property of chebyshev polynomials leads to good results , which are demonstrated with examples.

Image pattern classification is considered a significant step for image and video processing. Although various image pattern algorithms have been proposed so far that achieved adequate classification, achieving higher accuracy while reducing the computation time remains challenging to date. A robust image pattern classification method is essential to obtain the desired accuracy. This method can be accurately classify image blocks into plain, edge, and texture (PET) using an efficient feature extraction mechanism. Moreover, to date, most of the existing studies are focused on evaluating their methods based on specific orthogonal moments, which limits the understanding of their potential application to various Discrete Orthogonal Moments (DOM

In many scientific fields, Bayesian models are commonly used in recent research. This research presents a new Bayesian model for estimating parameters and forecasting using the Gibbs sampler algorithm. Posterior distributions are generated using the inverse gamma distribution and the multivariate normal distribution as prior distributions. The new method was used to investigate and summaries Bayesian statistics' posterior distribution. The theory and derivation of the posterior distribution are explained in detail in this paper. The proposed approach is applied to three simulation datasets of 100, 300, and 500 sample sizes. Also, the procedure was extended to the real dataset called the rock intensity dataset. The actual dataset is collecte