Multiplicative inverse in GF (2 m ) is a complex step in some important application such as Elliptic Curve Cryptography (ECC) and other applications. It operates by multiplying and squaring operation depending on the number of bits (m) in the field GF (2 m ). In this paper, a fast method is suggested to find inversion in GF (2 m ) using FPGA by reducing the number of multiplication operations in the Fermat's Theorem and transferring the squaring into a fast method to find exponentiation to (2 k ). In the proposed algorithm, the multiplicative inverse in GF(2 m ) is achieved by number of multiplications depending on log 2 (m) and each exponentiation is operates in a single clock cycle by generating a reduction matrix for high power of two ex

In this research, the possibility of using waste wooden materials (reed and sawdust) was studied to produce sustainable and thermal insulation lightweight building units , which has economic and environmental advantages. This study is intended to produce light weight building units with low thermal conductivity, so it can be used as partitions to improve the thermal insulation in buildings. Waste wooden materials were used as a partial replacement of natural sand, in different percentages (10, 20, 30, and 40) % . The mix proportions were (1:2.5) (cement: fine aggregate) with w/c of 0.4. The values of 28 days oven dry density ranged between (2060-1693) kg/m³.The thermal conductivity decreased from (0.745 to 0.2

We have studied Bayesian method in this paper by using the modified exponential growth model, where this model is more using to represent the growth phenomena. We focus on three of prior functions (Informative, Natural Conjugate, and the function that depends on previous experiments) to use it in the Bayesian method. Where almost of observations for the growth phenomena are depended on one another, which in turn leads to a correlation between those observations, which calls to treat such this problem, called Autocorrelation, and to verified this has been used Bayesian method.

The goal of this study is to knowledge the effect of Autocorrelation on the estimation by using Bayesian method. F

The Hopfield network is one of the easiest types, and its architecture is such that each neuron in the network connects to the other, thus called a fully connected neural network. In addition, this type is considered auto-associative memory, because the network returns the pattern immediately upon recognition, this network has many limitations, including memory capacity, discrepancy, orthogonally between patterns, weight symmetry, and local minimum. This paper proposes a new strategy for designing Hopfield based on XOR operation; A new strategy is proposed to solve these limitations by suggesting a new algorithm in the Hopfield network design, this strategy will increase the performance of Hopfield by modifying the architecture of t