Optimization technique for frequency estimation: Avoiding local minima

Harmonic minimization in PWM inverters is a complex optimization problem which involves non-linear transcendental equations having multiple local minima. In this paper, a solution to the harmonic minimization problem using the particle swarm optimization (PSO) has been proposed to solve the SHE problem. It overcomes the complicated computations associated with conventional iterative techniques. As an example, in this paper a bipolar PWM inverter with seven switching angles per quarter cycle with varying modulation index is considered, and the optimum switching angles are calculated offline to eliminate the 5th,7th, 11th,13th,17th and 19th harmonics. The simulated results are also validated by experiment.

Download Full-text

RIDGE FREQUENCY ESTIMATION FOR LOW-QUALITY FINGERPRINT IMAGES ENHANCEMENT USING DELAUNAY TRIANGULATION

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001414560023 ◽

2014 ◽

Vol 28 (01) ◽

pp. 1456002 ◽

Cited By ~ 1

Author(s):

RAJA LEHTIHET ◽

WAEL EL ORAIBY ◽

MOHAMMED BENMOHAMMED

Keyword(s):

Computational Geometry ◽

Uniform Distribution ◽

Delaunay Triangulation ◽

Error Rate ◽

Frequency Estimation ◽

Local Minima ◽

Equal Error Rate ◽

Fingerprint Image ◽

Points Of Interest ◽

Anisotropic Filter

In this paper, we propose a hybrid computational geometry-gray scale algorithm that enhances fingerprint images greatly. The algorithm extracts the local minima points that are positioned on the ridges of a fingerprint, then, it generates a Delaunay triangulation using these points of interest. This triangulation along with the local orientations give an accurate distance and orientation-based ridge frequency. Finally, a tuned anisotropic filter is locally applied and the enhanced output fingerprint image is obtained. When the algorithm is applied to rejected fingerprint images from FVC2004 DB2 database by the veryfinger application, these images pass and experimental results show that we obtain a low false and missed minutiae rate with an almost uniform distribution over the database. Moreover, the application of the proposed algorithm enables the extraction of features from all low-quality fingerprint images where the equal error rate of verification is decreased from 6.50% to 5% using nondamaged low-quality images in the database.

Download Full-text

Scan line optimization for Tri stereo planetary images

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xl-3-33-2014 ◽

2014 ◽

Vol XL-3 ◽

pp. 33-37

Author(s):

R. H. Bhalerao ◽

S. S. Gedam ◽

J. Joglekar

Keyword(s):

Global Minimum ◽

Optimization Technique ◽

Direct Methods ◽

Ground Truth ◽

Optimization Method ◽

Local Minima ◽

It Use ◽

Laser Altimeter ◽

Lunar Reconnaissance Orbiter ◽

Scan Line

In this paper, we propose a new scan line optimization method for matching the triplet of images. In the present paper, the triplets are initially matched using an area based local method. The cost is stored in a structure called as the Disparity Space Image (DSI). Using the global minimum of this cost the initial disparity is generated. Next the local minima are considered as potential matches where global minimum gives erroneous results. These local minima are used for optimization of disparity. As the method is a scanned line optimization, it use popularly resampled images. The experiment is performed using Terrain Mapping Camera images from the Chandrayaan-1 mission. In order to validate the result for accuracy, Lunar Orbiter Laser Altimeter dataset from Lunar Reconnaissance Orbiter mission is used. The method is again verified using standard Middlebury stereo dataset with ground truth. From experiments, it has been observed that using optimization technique for triplets, the total number of correct matches has increased by 5–10 % in comparison to direct methods. The method particularly gives good results at smooth regions, where dynamic programming and blockmatching gives limited accuracy.

Download Full-text

NON-LINEAR GLOBAL OPTIMIZATION VIA PARAMETERIZATION AND INVERSE FUNCTION APPROXIMATION: AN ARTIFICIAL NEURAL NETWORKS APPROACH

International Journal of Neural Systems ◽

10.1142/s0129065707001202 ◽

2007 ◽

Vol 17 (05) ◽

pp. 353-368 ◽

Cited By ~ 6

Author(s):

RENÉ V. MAYORGA ◽

MARIANO ARRIAGA

Keyword(s):

Global Optimization ◽

Optimization Technique ◽

Inverse Function ◽

Optimization Methods ◽

Computational Effort ◽

Linear Functions ◽

Local Minima ◽

Non Linear ◽

Artificial Neural ◽

Artificial Neural Network Ann

In this article, a novel technique for non-linear global optimization is presented. The main goal is to find the optimal global solution of non-linear problems avoiding sub-optimal local solutions or inflection points. The proposed technique is based on a two steps concept: properly keep decreasing the value of the objective function, and calculating the corresponding independent variables by approximating its inverse function. The decreasing process can continue even after reaching local minima and, in general, the algorithm stops when converging to solutions near the global minimum. The implementation of the proposed technique by conventional numerical methods may require a considerable computational effort on the approximation of the inverse function. Thus, here a novel Artificial Neural Network (ANN) approach is implemented to reduce the computational requirements of the proposed optimization technique. This approach is successfully tested on some highly non-linear functions possessing several local minima. The results obtained demonstrate that the proposed approach compares favorably over some current conventional numerical (Matlab functions) methods, and other non-conventional (Evolutionary Algorithms, Simulated Annealing) optimization methods.

Download Full-text

Medical Image Compression using Neural Network with HGAPSO Optimization

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.b6616.129219 ◽

2019 ◽

Vol 9 (2) ◽

pp. 3505-3509

Keyword(s):

Neural Network ◽

Image Compression ◽

Network Architecture ◽

Medical Image ◽

Optimization Technique ◽

Training Algorithms ◽

Local Minima ◽

Back Propagation Algorithm ◽

Medical Image Compression ◽

Proper Training

Lossy medical image compression has become increasingly attractive due to a drastic increase in the number of images used for diagnosis and treatment. The work focused on developing a feed-forward neural network for compression of medical images with optimization of weights using hybrid genetic and particle swarm (HGAPSO) optimization technique. The neural network can achieve a better compressed & decompressed image only with the proper training and optimized weights. Training algorithms such as back-propagation algorithm (BPA) traps to local minima rather than the global one which degrades the quality of a reconstructed image. In this work, HGAPSO optimization is adopted to overcome the drawback of BPA. HGAPSO parameters are carefully chosen to have better exploitation & exploration in the search area, which avoids the algorithm from trapping to the local minima. High-quality results of Genetic Algorithm (GA) obtained using selection, crossover and mutation can provide quality guidance for PSO which improves the results of the proposed system. The performance of the proposed work is evaluated on a raw medical image database based on PSNR, MSE, and CR. The experiment is simulated for 16-4-16 neural network architecture and a compression ratio of 75 % is achieved. The results obtained indicated that with proper training PSNR could be improved by 1.98 %.

Download Full-text