Image compression is one of the data compression types applied to digital images in order to reduce their high cost for storage and/or transmission. Image compression algorithms may take the benefit of visual sensitivity and statistical properties of image data to deliver superior results in comparison with generic data compression schemes, which are used for other digital data. In the first approach, the input image is divided into blocks, each of which is 16 x 16, 32 x 32, or 64 x 64 pixels. The blocks are converted first into a string; then, encoded by using a lossless and dictionary-based algorithm known as arithmetic coding. The more occurrence of the pixels values is codded in few bits compare with pixel values of less occurrence through the sub intervals between the range 0 and 1. Finally, the stream of compressed tables is reassembled for decompressing (image restoration). The results showed a compression gain of 10-12% and less time consumption when applying this type of coding to each block rather than the entire image. To improve the compression ratio, the second approach was used based on the YCbCr colour model. In this regard, images were decomposed into four sub-bands (low-low, high-low, low-high, and high-high) by using the discrete wavelet transform compression algorithm. Then, the low-low sub-band was transmuted to frequency components (low and high) via discrete wavelet transform. Next, these components were quantized by using scalar quantization and then scanning in a zigzag way. The compression ratio result is 15.1 to 27.5 for magnetic resonance imaging with a different peak signal to noise ratio and mean square error; 25 to 43 for X-ray images; 32 to 46 for computed tomography scan images; and 19 to 36 for magnetic resonance imaging brain images. The second approach showed an improved compression scheme compared to the first approach considering compression ratio, peak signal to noise ratio, and mean square error.
تُعبّرُ الصُّورةُ الحسَّيةُ في شعرِ ابن دُنَينير الموصليِّ([i]) في بنيتها عن تجربةِ الشاعرِ الوجدانيةِ والذهنيةِ, وأفكارهِ ومشاعرِهِ؛ فيصوغُ بها مَفهومًا جديدًا للواقعِ الماديِّ والمعنويِّ، الذي يتسمُ بالوضوحِ أولاً، وبالقرْبِ من الذهنِ ثانيًا، للربْطِ بين الحواسِّ الإنسانيةِ والمعاني الذهنيةِ، لِتُقَدِّمَ الصُّورةُ الحسيَّةُ إلى ((المتلقي صُورًا مرئيةً، يُعادُ تشكيلُها سياق
... Show MoreThis research studies the effect of adding micro, nano and hybrid by ratio (1:1) of (Al2O3,TiO2) to epoxy resin on thermal conductivity before and after immersion in HCl acid for (14 day) with normality (0.3 N) at weight fraction (0.02, 0.04, 0.06, 0.08) and thickness (6mm). The results of thermal conductivity reveled that epoxy reinforced by (Al2O3) and mixture (TiO2+Al2O3) increases with increasing the weight fraction, but the thermal conductivity (k) a values for micro and Nano (TiO2) decrease with increasing the weight fraction of reinforced, while the immersion in acidic solution (HCl) that the (k) values after immersion more than the value in before immersion.
In this research, the structural behavior of reinforced concrete columns made of normal and hybrid reactive powder concrete (hybrid by steel and polypropylene fibers) subjected to chloride salts with concentration was 8341.6 mg/l. The study consists of two parts, the first one is experimental study and the second one is theoretical analysis. Three main variables were adopted in the experimental program; concrete type, curing type and loading arrangement. Twenty (120x120x1200) mm columns were cast and tested depending on these variables. The samples were reinforced using two different bars; Ø8 for ties and Ø12 with minimum longitudinal reinforcement (0.01Ag). The specimens were divided into two main groups based o
... Show MorePreparation of epoxy/ TiO2 and epoxy/ Al2O3 nanocomposites is studed and investigated in this paper. The nano composites are processed by different nano fillers concentrations (0, 0.01, 0.02 ,0.03, 0.04 ,0.05 ,0.07 and 0.1 wt%). The particles sized of TiO2,Al2O3 are about 20–50 nm.Epoxy resin and nano composites containing different shape nano fillers of (TiO2:Al2O3 composites),are shear mixing with ratio 1 to 1,with different nano hybrid fillers concentrations( 0.025 ,0.0 5 ,0.15 ,0.2, and 0.25 wt%) to Preparation of epoxy/ TiO2- Al2O3 hybrid composites. The mechanical properties of nanocomposites such as bending ,wearing, and fatigue are investigated as mechanical properties.
This study presents a practical method for solving fractional order delay variational problems. The fractional derivative is given in the Caputo sense. The suggested approach is based on the Laplace transform and the shifted Legendre polynomials by approximating the candidate function by the shifted Legendre series with unknown coefficients yet to be determined. The proposed method converts the fractional order delay variational problem into a set of (n + 1) algebraic equations, where the solution to the resultant equation provides us the unknown coefficients of the terminated series that have been utilized to approximate the solution to the considered variational problem. Illustrative examples are given to show that the recommended appro
... Show MoreThis paper proposes a new approach, of Clustering Ultrasound images using the Hybrid Filter (CUHF) to determine the gender of the fetus in the early stages. The possible advantage of CUHF, a better result can be achieved when fuzzy c-mean FCM returns incorrect clusters. The proposed approach is conducted in two steps. Firstly, a preprocessing step to decrease the noise presented in ultrasound images by applying the filters: Local Binary Pattern (LBP), median, median and discrete wavelet (DWT), (median, DWT & LBP) and (median & Laplacian) ML. Secondly, implementing Fuzzy C-Mean (FCM) for clustering the resulted images from the first step. Amongst those filters, Median & Lap
This paper proposes a new approach, of Clustering Ultrasound images using the Hybrid Filter (CUHF) to determine the gender of the fetus in the early stages. The possible advantage of CUHF, a better result can be achieved when fuzzy c-mean FCM returns incorrect clusters. The proposed approach is conducted in two steps. Firstly, a preprocessing step to decrease the noise presented in ultrasound images by applying the filters: Local Binary Pattern (LBP), median, median and discrete wavelet (DWT),(median, DWT & LBP) and (median & Laplacian) ML. Secondly, implementing Fuzzy C-Mean (FCM) for clustering the resulted images from the first step. Amongst those filters, Median & Laplace has recorded a better accuracy. Our experimental evaluation on re
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