Engineering and Technology Journal

Botnet is a general term referring to a group of automated software robots that run without human intervention (malware code). Nowadays, Botnets produces a major threat to the cyber security (Information Assurance) of computing assets. Therefore, you need to protect our huge confidential and personal information through the use of web interfaces such as online passwords, corporate secrets, online banking accounts, and social networking accounts like Facebook. Network traffic analysis is an important component in the management and security of current networks and in the design and planning on future networks. This study enables the researcher: (a) to study botnet topologies, behavior and lifecycle events and actions (b) to combine normal web traffic and normal P2P traffic for binary classification; (c) to produce simulated network flow data comparable to the activities of a botnet controller or "bots," and hosts under attack (testing samples); and (d) to detection and identifies P2P botnet framework using Support Vector Machine (SVM) based on statistical features.

GPR is frequently used for inspection road pavement (thickness estimation, damage detection and diagnosis), so this technique was selected for this purpose. Three types of antennas were used: 250, 500 and 800 MHz. In the field, the survey has been carried out on one lanes paved road about 1250 m long inside University of Technology Campus. The obtained radargrams raw data were analyzed using sophisticated softwares to determine asphalt and concrete layers thicknesses for base and subase. Several inspections have been carried out to study the effect of changing the operating setting parameters of GPR on the data accuracy and interpretation for paved roads damages and the pavement layers thicknesses. The interpretation results, using 250 MHz antenna, showed the possibility of identifying buried plastic pipe and the flexible pavement layer. While the rigid pavement layer is unresolved. With 500 MHz antenna, the plastic pipe, rigid pavement, steel reinforcement bars and joint sheet cork appeared clearly. It was found that the short type of the Max. Time Window using antenna 800 MHz is the most suitable for detecting some radar anomalies (plastic pipe, cork sheet and reinforcement bars and pavement thickness) which were more clearly defined. No change is noticed on radargrams when applying different point intervals. The suitable radar wave velocity for estimating the thicknesses of the surface, binder and rigid pavement layers were 80, 160 and 180 m/ns respectively. While, the surface cracks were undetectable with 800 MHz antenna but the cause of such crack was identified. Correlation GPR thickness data with the asphalt core data, states that the error of the thickness measurements from GPR resulted in average deviation (the percentage error) of about 4%.

A soliton is a solitary wave whose amplitude, shape, and velocity are conserved after a collision with another soliton. Solitons, in general, manifest themselves in a large variety of wave/particle systems in nature: practically in any system that possesses both dispersion (in time or space) and nonlinearity. Solitons have been identified in optics, plasmas, fluids, condensed matter, particle physics, and astrophysics. Yet over the past decade, the forefront of soliton research has shifted to neuroscience. The Soliton model in neuroscience is a recently developed model that attempts to explain how signals are conducted within neurons. It proposes that the signals travel along the cell's membrane in the form of certain kinds of sound (or density) pulses known as solitons. The electrocardiogram (ECG) signal is generated by the rhythmic contractions of the heart. It represents the electrical activity of the heart muscles, and is usually measured by the electrodes placed on body surface. Electrocardiogram (ECG) signal has been widely used in cardiac pathology to detect heart disease. In this paper, Soliton Feed forward Neural Network (SFNN) is proposed for ECG signal enhancement. Computer simulation results demonstratedthat the proposed approach can successfully be used to model the ECG signal and remove high-frequency noise

In the present work, theoretical and experimental investigation have been carried out with two hydrocarbon refrigerant mixtures as a possible alternatives to R134a. Theoretical performance of an ideal vapor compression refrigeration cycle using R290/R600 and R290/R600a was studied and the results were compared with those of R134a. The performance of the hydrocarbon mixtures of R290/R600 and R290/R600a were experimentally investigated as a drop-in alternatives to replace R134a in 297 liter domestic refrigerator designed to work with R134a. The results showed a lower freezer air temperature, lower cabinet air temperature, lower discharge temperature, higher coefficient of performance, lower pressure ratio and lower electrical power by about 3.2°C, 2.3°C, 3.1°C, 4.9%, 7% and 9.4%, respectively, for R290/R600 (60/40) % with 60g mass charge and 50% longer capillary tube compared with those of R134a with 120g mass charge.

Recycling wastes are a practical solution to reduce its detrimental effects. Many problems have arisen recently due to the rapid expansion on the quantity of waste. In this work, a practical solution was presented. The waste was mixed with wet clay to form a mixture that molded under pressure to form a wet brick. The wet bricks were entered into big ovens and during the firing process, porosity was induced inside them. The induced porosity enhanced the thermal insulation, decreasing the density, producing good brick durability with acceptable mechanical properties of the fired brick, in addition to that, a preservation in natural resources could be achieved. Following ASTM standards the values of the specific heat, dry density, thermal conductivity, cold and boiling absorption coefficient and both wet and dry compressive strength were measured with good accuracy

A geometric shapes recognition algorithm is proposed in this paper. This algorithm is interactive which means that if not all the desired shapes are recognized then the recognition process is repeated by the user to improve the capability of the process.
The user can change threshold value when converting the image from gray to binary many times during one program execution to get the required results.
The proposed algorithm can select the desired shape individually even if there are
different shapes in the image (multi-shapes image).
This algorithm is software implemented using Matlab programming language and then
implemented program is used to recognize any of the following shapes (the shapes
must be distinct and not overlapped):
Seven types of triangle; pentagons, hexagons, squares, rectangles, circles, ellipse shapes, rhombus, and parallelogram.
The recognition capability of the implemented software is tested for different cases and from these cases many points are concluded.

Wind energy became one of the techniques that attracted much attention worldwide. The induction generator is used in the exploitation of this energy and converts it into electrical energy because of the advantages that distinguish it from other types of generators. In this paper, an optimal identification of induction generator parameters is proposed. Particle Swarm Optimization technique (PSO) trained using Artificial Neural Network (ANN) is used to identify the main parameters of the induction generator in cases of wind speed change, load change and fault cases.
The simulation results obtained indicate that the particle swarm optimization is suitable for neural networks training for controlling of the voltage, frequency and generated power. The simulation programming is implemented using MATLAB.

In the present Work, effect of substrate temperatures on the optical and morphological properties of In2O3 trioxide thin film has been carried out using Reactive Pulsed Laser as a Deposition technique (RPLD). 1.064μm, 7 nsec Q-switch Nd-YAG laser with 400 mJ/cm2 laser energy’s has been used to ablated pure Indium target and deposited on glass substrates . The resulted films show High transparency reached to about (85) % which found to decrease sharply with the substrate temperatures. The estimated optical band gap found to be about 3.6eV at optimum substrate temperatures (70 Co). The FTIR results insure the formation ofIn-O vibrational bond with different vibrational intensity depending on substrate temperatures

Feed forward artificial neural network has been applied to predict the quality of turned surfaces for two types of coated carbide inserts. Four networks were proposed for each insert. The networks have been trained and tested using a former experimental data. The input data, represented by cutting parameter values, and output data, represented by surface roughness, were fed into the network model. Each network has three layers adopted for prediction. The first one is the input layer which involves cutting parameters: cutting speed, feed rate, and depth of cut; the second layer is hidden layer consisting of two hidden layers. The third layer of the network is the output layer which gives the surface roughness value. Levenberg - Marquardt algorithm is used in the back-propagation algorithm to train these networks. The best result was obtained for networks which have (12) neurons in the first hidden layer and (9) neurons in the second hidden layer. These networks had given and mean square error = 0.0033 for the first insert, whereas, for the second insert, and mean square error = 0.0023. These networks were used to predict the optimum cutting parameters which give minimum surface roughness.