Mohammadreza Ashouri

Mohammadreza Ashouri

In this paper, a new stream cipher is designed as a clock-controlled one, but with a new mechanism of altering steps based on system theory in such a way that the structures used in it are resistant to conventional attacks. Our proposed algorithm (PALS) uses the main key with the length of 256 bits and a 32-bit message key. The most important criteria considered in designing the PALS are resistance to known attacks, maximum period, high linear complexity, and good statistical properties. As a result, the output keystream is very similar to the perfectly random sequences and resistant to conventional attacks such as correlation attacks, algebraic attack, divide & conquer attack, time-memory tradeoff attack and AIDA/cube attacks. The base structure of the PALS is a clock-controlled combination generator with memory and we obtained all the features according to design criteria with this structure. PALS can be used in many applications, especially in financial cryptography due to its proper security features

Nouraddin Misagh, Mohammadreza Ashouri

Exploration of hydrocarbon resources is a highly complicated and expensive process where various geological, geochemical and geophysical factors are developed then combined together. It is highly significant how to design the seismic data acquisition survey and locate the exploratory wells since incorrect or imprecise locations lead to waste of time and money during the operation. The objective of this study is to locate high-potential oil and gas field in 1: 250,000 sheet of Ahwaz including 20 oil fields to reduce both time and costs in exploration and production processes. In this regard, 17 maps were developed using GIS functions for factors including: minimum and maximum of total organic carbon (TOC), yield potential for hydrocarbons production (PP), Tmax peak, production index (PI), oxygen index (OI), hydrogen index (HI) as well as presence or proximity to high residual Bouguer gravity anomalies, proximity to anticline axis and faults, topography and curvature maps obtained from Asmari Formation subsurface contours. To model and to integrate maps, this study employed artificial neural network and adaptive neuro-fuzzy inference system (ANFIS) methods. The results obtained from model validation demonstrated that the 17x10x5 neural network with R=0.8948, RMS=0.0267, and kappa=0.9079 can be trained better than other models such as ANFIS and predicts the potential areas more accurately. However, this method failed to predict some oil fields and wrongly predict some areas as potential zones.