|Commenced in January 1999 || Frequency: Monthly || Edition: International|| Paper Count: 6 |
Mathematical, Computational, Physical, Electrical and Computer Engineering
Towards Finite Element Modeling of the Accoustics of Human Head
In this paper, a new formulation for acoustics coupled with linear elasticity is presented. The primary objective of the work is to develop a three dimensional hp adaptive finite element method code destinated for modeling of acoustics of human head. The code will have numerous applications e.g. in designing hearing protection devices for individuals working in high noise environments. The presented work is in the preliminary stage. The variational formulation has been implemented and tested on a sequence of meshes with concentric multi-layer spheres, with material data representing the tissue (the brain), skull and the air. Thus, an efficient solver for coupled elasticity/acoustics problems has been developed, and tested on high contrast material data representing the human head.
Assessment of the Effect of Feed Plate Location on Interactions for a Binary Distillation Column
The paper considers the effect of feed plate location
on the interactions in a seven plate binary distillation column. The
mathematical model of the distillation column is deduced based on
the equations of mass and energy balances for each stage, detailed
model for both reboiler and condenser, and heat transfer equations.
The Dynamic Relative Magnitude Criterion, DRMC is used to assess
the interactions in different feed plate locations for a seven plate
(Benzene-Toluene) binary distillation column ( the feed plate is
originally at stage 4). The results show that whenever we go far from
the optimum feed plate position, the level of interaction augments.
Prediction of Bath Temperature Using Neural Networks
In this work, we consider an application of neural networks in LD converter. Application of this approach assumes a reliable prediction of steel temperature and reduces a reblow ratio in steel work. It has been applied a conventional model to charge calculation, the obtained results by this technique are not always good, this is due to the process complexity. Difficulties are mainly generated by the noisy measurement and the process non linearities. Artificial Neural Networks (ANNs) have become a powerful tool for these complex applications. It is used a backpropagation algorithm to learn the neural nets. (ANNs) is used to predict the steel bath temperature in oxygen converter process for the end condition. This model has 11 inputs process variables and one output. The model was tested in steel work, the obtained results by neural approach are better than the conventional model.
Hybrid Coding for Animated Polygonal Meshes
A new hybrid coding method for compressing
animated polygonal meshes is presented. This paper assumes
the simplistic representation of the geometric data: a temporal
sequence of polygonal meshes for each discrete frame of the
animated sequence. The method utilizes a delta coding and an
octree-based method. In this hybrid method, both the octree
approach and the delta coding approach are applied to each
single frame in the animation sequence in parallel. The
approach that generates the smaller encoded file size is chosen
to encode the current frame. Given the same quality
requirement, the hybrid coding method can achieve much
higher compression ratio than the octree-only method or the
delta-only method. The hybrid approach can represent 3D
animated sequences with higher compression factors while
maintaining reasonable quality. It is easy to implement and have
a low cost encoding process and a fast decoding process, which
make it a better choice for real time application.
New Analysis Methods on Strict Avalanche Criterion of S-Boxes
S-boxes (Substitution boxes) are keystones of modern
symmetric cryptosystems (block ciphers, as well as stream ciphers).
S-boxes bring nonlinearity to cryptosystems and strengthen their
cryptographic security. They are used for confusion in data security
An S-box satisfies the strict avalanche criterion (SAC), if and only if
for any single input bit of the S-box, the inversion of it changes each
output bit with probability one half. If a function (cryptographic
transformation) is complete, then each output bit depends on all of
the input bits. Thus, if it were possible to find the simplest Boolean
expression for each output bit in terms of the input bits, each of these
expressions would have to contain all of the input bits if the function
is complete. From some important properties of S-box, the most
interesting property SAC (Strict Avalanche Criterion) is presented
and to analyze this property three analysis methods are proposed.
Efficient Program Slicing Algorithms for Measuring Functional Cohesion and Parallelism
Program slicing is the task of finding all statements in a program that directly or indirectly influence the value of a variable occurrence. The set of statements that can affect the value of a variable at some point in a program is called a program slice. In several software engineering applications, such as program debugging and measuring program cohesion and parallelism, several slices are computed at different program points. In this paper, algorithms are introduced to compute all backward and forward static slices of a computer program by traversing the program representation graph once. The program representation graph used in this paper is called Program Dependence Graph (PDG). We have conducted an experimental comparison study using 25 software modules to show the effectiveness of the introduced algorithm for computing all backward static slices over single-point slicing approaches in computing the parallelism and functional cohesion of program modules. The effectiveness of the algorithm is measured in terms of time execution and number of traversed PDG edges. The comparison study results indicate that using the introduced algorithm considerably saves the slicing time and effort required to measure module parallelism and functional cohesion.