In this paper a new technique is developed on stellar spectral classification. Because stellar spectral data sets are usually extremely noisy, wavelet de-noising method is proposed to reduce noise first. Then the support vector machines (SVM) is used for the classification. Experimental results show that in most cases, there will be a better performance using this composite classifier than usin...

De-noising algorithms based on wavelet thresholding replace small wavelet coeecients by zero and keep or shrink the coeecients with absolute value above the threshold. The optimal threshold minimizes the error of the result as compared to the unknown, exact data. To estimate this optimal threshold, we use Generalized Cross Validation. This procedure does not require an estimation for the noise ...

This paper addressed a framework of a traffic prediction model which could eliminate the noises caused by random travel conditions. In the meantime, this model can also quantitatively calculate the influence of special factors. This framework combined several artificial intelligence technologies such as wavelet transform, neural network, and fuzzy logic. In addition to developing the prediction...

Image de-noising is one of the fundamental problems in the field of image processing needed for improving the image quality before performing different high-level vision tasks. Numerous wavelet based denoising methods were utilized for performing image de-noising process. In such works, there is a lack of analysis in selecting the appropriate threshold value. Moreover, such analysis leads to th...

Ground-based synthetic aperture radar (GB-SAR), as a new non-contact measurement technique, has been widely applied to obtain the dynamic deflection of various bridges without corner reflectors. However, it will cause some high-frequency noise in obtained with low signal-to-noise ratio. To solve this problem, paper proposes an innovative de-noising method combining wavelet synchro-squeezing tra...

Here it is represented an image analysis technique using both noising & de-noising process. By taking a simple image of different formats we added a noise i. e. Gaussian noise to the particular image and then the calculation of SNR(SIGNAL-TO-NOISE RATIO) and PSNR(PEAK SIGNAL-TO-NOISE RATIO) is performed based on the image formats such as jpeg, png, etc. Although there are various types of noise...

De-noising is one of the most important applications of image processing which has been applied to a wide variety of real world problems. De-noising allows for improving image quality in imaging modalities that are noise prone. A lot of research work has gone in to improving quality of 2D images using various de-noising techniques but new modalities of imaging such as industrial 3D X-ray comput...

seismic waves are non-stationary due to its propagation through the earth. time-frequency transformsare suitable tools for analyzing non-stationary seismic signals. spectral decomposition can reveal thenon-stationary characteristics which cannot be easily observed in the time or frequency representationalone. various types of spectral decomposition methods have been introduced by some researche...