Generally, direct measurement of soil compression index (Cc) is expensive and time-consuming. To save time and effort, indirect methods to obtain Cc may be an inexpensive option. Usually, the indirect methods are based on a correlation between some easier measuring descriptive variables such as liquid limit, soil density, and natural water content. This study used the ANFIS and regression methods to obtain Cc indirectly. To achieve the aim of this investigation, 177 undisturbed samples were collected from the cohesive soil in Sulaymaniyah Governorate in Iraq. Results of this study indicated that ANFIS models over-performed the Regression method in estimating Cc with R² of 0.66 and 0.48 for both ANFIS and Regre

Compression is the reduction in size of data in order to save space or transmission time. For data transmission, compression can be performed on just the data content or on the entire transmission unit (including header data) depending on a number of factors. In this study, we considered the application of an audio compression method by using text coding where audio compression represented via convert audio file to text file for reducing the time to data transfer by communication channel. Approach: we proposed two coding methods are applied to optimizing the solution by using CFG. Results: we test our application by using 4-bit coding algorithm the results of this method show not satisfy then we proposed a new approach to compress audio fil

The variation of compression index C_c and swelling index C_s with the degree of saturation S was studied on unsaturated and fully saturated soils for different degrees of saturation (100%, 91%, 85%, 75%, 60%), several mathematical equations were found to describe these relationships, these equations can be used to predict settlement during the consolidation process in unsaturated and fully saturated soils.

In this paper, we investigate the automatic recognition of emotion in text. We perform experiments with a new method of classification based on the PPM character-based text compression scheme. These experiments involve both coarse-grained classification (whether a text is emotional or not) and also fine-grained classification such as recognising Ekman’s six basic emotions (Anger, Disgust, Fear, Happiness, Sadness, Surprise). Experimental results with three datasets show that the new method significantly outperforms the traditional word-based text classification methods. The results show that the PPM compression based classification method is able to distinguish between emotional and nonemotional text with high accuracy, between texts invo

In this paper, visible image watermarking algorithm based on biorthogonal wavelet
transform is proposed. The watermark (logo) of type binary image can be embedded in the
host gray image by using coefficients bands of the transformed host image by biorthogonal
transform domain. The logo image can be embedded in the top-left corner or spread over the
whole host image. A scaling value (α) in the frequency domain is introduced to control the
perception of the watermarked image. Experimental results show that this watermark
algorithm gives visible logo with and no losses in the recovery process of the original image,
the calculated PSNR values support that. Good robustness against attempt to remove the
watermark was s

Image recognition is one of the most important applications of information processing, in this paper; a comparison between 3-level techniques based image recognition has been achieved, using discrete wavelet (DWT) and stationary wavelet transforms (SWT), stationary-stationary-stationary (sss), stationary-stationary-wavelet (ssw), stationary-wavelet-stationary (sws), stationary-wavelet-wavelet (sww), wavelet-stationary- stationary (wss), wavelet-stationary-wavelet (wsw), wavelet-wavelet-stationary (wws) and wavelet-wavelet-wavelet (www). A comparison between these techniques has been implemented. according to the peak signal to noise ratio (PSNR), root mean square error (RMSE), compression ratio (CR) and the coding noise e (n) of each third

This paper presents a combination of enhancement techniques for fingerprint images affected by different type of noise. These techniques were applied to improve image quality and come up with an acceptable image contrast. The proposed method included five different enhancement techniques: Normalization, Histogram Equalization, Binarization, Skeletonization and Fusion. The Normalization process standardized the pixel intensity which facilitated the processing of subsequent image enhancement stages. Subsequently, the Histogram Equalization technique increased the contrast of the images. Furthermore, the Binarization and Skeletonization techniques were implemented to differentiate between the ridge and valley structures and to obtain one

Image Fusion Using A Convolutional Neural Network

Many image processing and machine learning applications require sufficient image feature selection and representation. This can be achieved by imitating human ability to process visual information. One such ability is that human eyes are much more sensitive to changes in the intensity (luminance) than the color information. In this paper, we present how to exploit luminance information, organized in a pyramid structure, to transfer properties between two images. Two applications are presented to demonstrate the results of using luminance channel in the similarity metric of two images. These are image generation; where a target image is to be generated from a source one, and image colorization; where color information is to be browsed from o