Microwave Tomography: The Ultimate Guide to Global Optimization, Parallelization, and Performance

Microwave tomography is a non-invasive imaging technique that uses microwaves to create images of objects. It has a wide range of applications in medical imaging, non-destructive testing, and industrial process monitoring.

Conventional microwave tomography algorithms are based on local optimization, which can be slow and prone to getting stuck in local minima. Global optimization algorithms, on the other hand, are more likely to find the global minimum, but they can be even slower and more computationally expensive.

Microwave Tomography: Global Optimization, Parallelization and Performance Evaluation

by Abas Sabouni

5 out of 5

Language	:	English
File size	:	11774 KB
Text-to-Speech	:	Enabled
Screen Reader	:	Supported
Enhanced typesetting	:	Enabled
Print length	:	215 pages

FREE

Parallelization is a technique that can be used to speed up the computation of microwave tomography algorithms. By distributing the computation across multiple processors, parallelization can significantly reduce the time it takes to reconstruct an image.

The performance of microwave tomography algorithms can be improved by optimizing the algorithm parameters and the hardware used to implement the algorithm. This can be done through a combination of theoretical analysis and experimental testing.

Global Optimization

Global optimization is a branch of mathematics that deals with finding the global minimum of a function. This is in contrast to local optimization, which finds the local minimum, which is not necessarily the global minimum.

There are a number of different global optimization algorithms, each with its own advantages and disadvantages. Some of the most popular global optimization algorithms include:

* Simulated annealing * Genetic algorithms * Particle swarm optimization * Differential evolution

The choice of which global optimization algorithm to use depends on the specific problem being solved.

Parallelization

Parallelization is a technique that can be used to speed up the computation of microwave tomography algorithms. By distributing the computation across multiple processors, parallelization can significantly reduce the time it takes to reconstruct an image.

There are a number of different parallelization techniques that can be used for microwave tomography. Some of the most common parallelization techniques include:

* OpenMP * MPI * CUDA

The choice of which parallelization technique to use depends on the specific hardware and software being used.

Performance

The performance of microwave tomography algorithms can be improved by optimizing the algorithm parameters and the hardware used to implement the algorithm. This can be done through a combination of theoretical analysis and experimental testing.

Some of the most important factors that affect the performance of microwave tomography algorithms include:

* The number of antennas used * The frequency of the microwaves * The size of the object being imaged * The amount of noise in the data

By optimizing these factors, it is possible to significantly improve the performance of microwave tomography algorithms.

Microwave tomography is a promising imaging modality with a wide range of applications. By using global optimization, parallelization, and performance optimization techniques, it is possible to significantly improve the accuracy, speed, and performance of microwave tomography algorithms.

This book provides a comprehensive overview of the theory and practice of microwave tomography, with a focus on global optimization, parallelization, and performance. The book is intended for researchers, engineers, and students who are interested in learning about the latest advances in microwave tomography.

Microwave Tomography: Global Optimization, Parallelization and Performance Evaluation

by Abas Sabouni

5 out of 5

Language	:	English
File size	:	11774 KB
Text-to-Speech	:	Enabled
Screen Reader	:	Supported
Enhanced typesetting	:	Enabled
Print length	:	215 pages

FREE