In this paper, Discrete-wavelet transform (DWT) based algorithm are used for speech signal denoising. Here both hard and soft thresholding are used for denoising. Analysis is done on noisy speech signal corrupted by babble noise at 0dB, 5dB, 10dB and 15dB SNR levels. Simulation & results are performed in MATLAB 7.10.0 (R2010a). Output SNR (Signal to Noise Ratio) and MSE (Mean Square Error) is c...

Thresholding is a type of image segmentation, where the pixels change to make easier analyze. In bi-level thresholding, in grayscale format transformed into binary format. The traditional methods for thresholding may be inefficient finding best threshold and take longer computation time. Recently, metaheuristic swarm-based algorithms were applied optimization different applications find optimal...

We demonstrate an enterprise Dynamic Thresholding System for data-agnostic management of monitoring flows. The dynamic thresholding based on data historical behavior enables adaptive and more accurate control of business environments compared to static thresholding. We manifest the main blocks of a complex analytical engine that is implemented in VMware vCenter Operations Manager as a principal...

We propose a soft thresholding approach to the minimum description length wavelet denoising. Our method is based on combining two-part coding with normalized maximum likelihood universal models to give a soft thresholding denoising criterion. Experiments with the proposed MDL soft thresholding method indicate that our denoising criterion leads to fairly similar performance as with the well-know...

The objective of this paper is to propose an adaptive-evolutionary method for thresholding which is used as an artificial intelligent algorithm for image segmentation especially for object segmentation. This method employs resistant versus mixed histograms because of its suitable fitness function selection that consists of the histogram details. As things develop in the paper, three evolutionar...

This paper proposes a local adaptive thresholding method based on a water #ow model, in which an image surface is considered as a three-dimensional (3-D) terrain. To extract characters from backgrounds, we pour water onto the terrain surface.Water #ows down to the lower regions of the terrain and "lls valleys. Then, the thresholding process is applied to the amount of "lled water for character ...

Variance-based thresholding method is a very effective technology for image segmentation. However, its performance is limited in traditional one-dimensional and twodimensional scheme. In this paper, a novel two-dimensional variance thresholding scheme to improve image segmentation performance is proposed. The two-dimensional histogram of the original and local average image is projected to one-...

The drawback of the conventional multilevel thresholding methods is high computational cost since they do exhaustive search among exponentialy growing number of possible thresholds to optimize the objective functions. In this paper a new multilevel thresholding method based on cuckoo search (CS) algorithm is proposed in order to overcome this obstacle. The optimal thresholds are found by maximi...

The multilevel thresholding problem is often treated as a problem of optimization of an objective function. This paper presents both adaptation and comparison of six meta-heuristic techniques to solve the multilevel thresholding problem: a genetic algorithm, particle swarm optimization, differential evolution, ant colony, simulated annealing and tabu search. Experiments results show that the ge...