In this paper, the analysis of recent advances in genetic algorithms is discussed. The genetic algorithms of great interest in research community are selected for analysis. This review will help the new and demanding researchers to provide the wider vision of genetic algorithms. The well-known algorithms and their implementation are presented with their pros and cons. The genetic operators and their usages are discussed with the aim of facilitating new researchers. The different research domains involved in genetic algorithms are covered. The future research directions in the area of genetic operators, fitness function and hybrid algorithms are discussed. This structured review will be helpful for research and graduate teaching.

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A review on genetic algorithm: past, present, and future

Wavelet transform based on inner product in fault diagnosis of rotating machinery: A review

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Two Dimensional Signal And Image Processing

/pdf/analysis-and-numerical-solution-of-generalized-lyapunov-3cti9y79me.pdf

Analysis and Numerical Solution of Generalized Lyapunov Equations

In order to identify the bacteria in wound infection, an electronic nose system with a sensor array of 34 sensors was designed. Eight kinds of samples were detected, i.e., culture medium, Escherichia coli , Staphylococcus aureus , Pseudomonas aeruginosa and their mixture with different concentration. Using support vector machine as the classifier and without sensor array optimization, the recognition rate is up to 86.54%. To simplify the sensor array and improve the recognition rate for bacteria samples, Wilks’ lambda statistic (Wilks’ Λ-statistic), Mahalanobis distance, principal component analysis (PCA), linear discriminant analysis (LDA), and genetic algorithm are used to optimize the sensor array. It is shown that the sensor array optimization may be realized efficiently by these methods except PCA. After sensor array optimization by Wilks’ Λ-statistic and LDA, both of their recognition rates are the highest and up to 96.15%, while the numbers of sensors in optimized sensor arrays are 22 and 20, respectively. Under the limitation of ten sensors, the recognition rate optimized by Wilks’ Λ-statistic and LDA may still reach 95.19%.

Sensor Array Optimization of Electronic Nose for Detection of Bacteria in Wound Infection

Image denoising is the process to remove the noise from the image naturally corrupted by the noise. The wavelet method is one among various methods for recovering infinite dimensional objects like curves, densities, images, etc. The wavelet techniques are very effective to remove the noise because of their ability to capture the energy of a signal in few energy transform values. The wavelet methods are based on shrinking the wavelet coefficients in the wavelet domain. We propose in this paper, a denoising approach basing on dual tree complex wavelet and shrinkage with the Wiener filter technique (where either hard or soft thresholding operators of dual tree complex wavelet transform for the denoising of medical images are used). The results proved that the denoised images using DTCWT (Dual Tree Complex Wavelet Transform) with Wiener filter have a better balance between smoothness and accuracy than the DWT and are less redundant than SWT (StationaryWavelet Transform). We used the SSIM (Structural Similarity Index Measure) along with PSNR (Peak Signal to Noise Ratio) and SSIM map to assess the quality of denoised images.

Medical image denoising using dual tree complex thresholding wavelet transform and Wiener filter

The aim of this letter is the construction of a new model order reduction algorithm generalized to the multi-input/multi-output systems model order reduction. It is essentially based on the complete order model dominant modes retention. This involves the conversion of the overall significant information contained in the original complete order system into the reduced order approximant, permitting the construction of our approximant denominator. The approximant numerator is computed by means of genetic algorithm (GA) tools and a square error criterion. As a result, an optimal approximant of lower order is derived. To show its performances, to highlight some important of its characteristics, and to conclude to its efficiency, a comparative study is carried out. A numerical example is given, where our approximant model is compared to reduced order models computed from two recent and important techniques based on GA tools, namely, the stability equation and the modified pole clustering techniques.

Multivariable Systems Model Reduction Based on the Dominant Modes and Genetic Algorithm

Image denoising is the process to remove the noise from the image naturally corrupted by the noise The wavelet method is one among various methods for recovering infinite dimensional objects like curves, densities, images,  etc The wavelet techniques are very effective to remove the noise because of their ability to capture the energy of a signal in few energy transform values The wavelet methods are based on shrinking the wavelet coefficients in the wavelet domain We propose in this paper, a denoising approach basing on dual tree complex wavelet and shrinkage (where either hard and soft thresholding operators of dual tree complex wavelet transform for the denoising of medical images are used) The results proved that the denoised images using DTCWT (Dual Tree Complex Wavelet Transform) have a better balance between smoothness and accuracy than the DWT and are less redundant than SWT (Stationary Wavelet Transform) We used the SSIM (Structural Similarity Index Measure) along with PSNR (Peak Signal to Noise Ratio) and SSIM Map to assess the quality of denoised images

Medical image denoising using dual tree complex thresholding wavelet transform

We present a comparative study of recent methods for simplifying large-scale systems via low-order models, such as the Schur, the balanced realizations, the balanced gains, and the optimal aggregation approaches that we group under the nomination of internal projections, defining a new class of model reduction scheme. These approaches use the state-space representation and develop algorithms in order to obtain a new low-order state-representation. Through an illustrative example, where all methods are applied for the simplification of a given digital prototype Cauer filter, a comparative performance study is done.

Comparative study of model reduction schemes - application to the digital filters synthesis

Two SVD-based techniques to construct different types of lower degree approximants of a singular system are presented. Such techniques enable to preserve the key properties of the original system, such as stability, and give a quantization of approximation error. The first technique consists in reducing only the proper part of the descriptor system, while the second allows to reduce at once the proper and the improper parts of the system. The numerical results reveal the capabilities of the proposed techniques.

Lahcène Mitiche

Papers

Medical image denoising using dual tree complex thresholding wavelet transform and Wiener filter

Multivariable Systems Model Reduction Based on the Dominant Modes and Genetic Algorithm

Medical image denoising using dual tree complex thresholding wavelet transform

Comparative study of model reduction schemes - application to the digital filters synthesis

Model reduction for descriptor systems