Saturday, January 25, 2020
Curvelet-based Bayesian Estimator for Speckle Suppression
Curvelet-based Bayesian Estimator for Speckle Suppression Curvelet-based Bayesian Estimator for Speckle Suppression in Ultrasound Imaging Abstract. Ultrasound images are inherently affected by speckle noise, and thus the reduction of this noise is a crucial pre-processing step for their successful interpretation. Bayesian estimation is a powerful signal estimation technique used for speckle noise removal in images. In the Bayesian-based despeckling schemes, the choice of suitable statistical models and the development of a shrinkage function for estimation of the noise-free signal are the major concerns. In this paper, a novel curvelet-based Bayesian estimation scheme for despeckling of ultrasound images is developed. The curvelet coefficients of the multiplicative degradation model of the noisy ultrasound image are additively decomposed into noise-free and signal-dependent noise components. The Cauchy and two-sided exponential distributions are assumed to be statistical models for the noise-free and signal-dependent noise components of the observed curvelet coefficients, respectively, and an efficient low-complexit y realization of the Bayesian estimator is proposed. The experimental results demonstrate the validity of the proposed despeckling scheme in providing a signifi cant suppression of the speckle noise and simultaneously preserving the image details. Keywords:Ultrasound imaging, curvelet transform, speckle noise, Bayesian estimation, statistical modeling. Introduction Ultrasound imaging is important for medical diagnosis and has the advantages of cost effectiveness, port-ability, acceptability and safety [1]. However, ultrasound images are of relatively poor quality due to its contamination by the speckle noise, which considerably degrades the image quality and leads to a negative impact on the diagnostic task. Thus, reducing speckle noise while preserving anatomic information is necessary to better delineate the regions of interest in the ultrasound images. In the work of speckle suppression in ultrasound images, many spatial-based techniques that employ either single-scale or multi-scale filtering have been developed in the literature [2-4]. Earlydeveloped single-scale spatial filtering [2] are limited in their capability for significantly reducing the speckle noise. More promising spatial single-scale techniques such as those using bilateral filtering [4] and nonlocal filtering [3] have been recently proposed. This work was supported in part by the Natural Sciences and Engineering Research Council (NSERC) of Canada and in part by the Regroupement Strategique en Microelectronique du Quebec (ReSMiQ). These techniques depend on the size of the fi lter window, and hence, for a satisfactory speckle suppression, they require large computational time. Alternatively, multi-scale spatial techniques [5], based on partial differential equations, have been investigated in the literature. These techniques are iterative and can produce smooth images with preserved edges. However, important structural details are unfortunately degraded during the iteration process. As an appropriate alternative to spatial-based speckle suppression in ultrasound images, many other despeckling techniques based on different transform domains, such as the ones of wavelet, contourlet, and curvelet, have been recently proposed in the literature [6-8]. Wavelet transform has a good reputation as a tool for noise reduction but has the drawback of poor directionality, which makes its usage limited in many applications. Using contourlet transform provides an improved noise reduction performance due to its property of fi‚exible directional decomposability. However, curvelet transform offers a higher directional sensitivity than that of contourlet transform and is more efficient in representing the curve-like details in images. For the development of despeckling techniques based on transform domains, thresholding [7] has been presented as a technique to build linear estimators of the noise-free signal coefficients. However, the main drawback of this thresholding technique is in the difficulty of determining a suitable threshold value. To circumvent this problem, non-linear estimators [6] have been statistically developed based on Bayesian estimation formalism. For the development of an efficient Bayesian-based despeckling scheme, the choice of a suitable probability distribution to model the transform domain coefficients is a major concern. Also, while investigating a suitable statistical model, the complexity of the Bayesian estimation process should be taken into consideration. Consequently, special attention should be paid to the realization complexity of the Bayesian estimator that results from employing the selected probabilistic model in one of the Bayesian frameworks. In this paper, to achieve a satisfactory performance for despeckling of ultrasound images at a lower computational effort, a new curvelet-based Bayesian scheme is proposed. The multiplicative degradation model representing an observed ultrasound image is decomposed into an additive model consisting of noise-free and signal-dependent noise components. Two-sided exponential distribution is used as a prior statistical model for the curvelet coefficients of the signal-dependant noise. This model, along with the Cauchy distribution is used to develop a low-complexity Bayesian estimator. The performance of the proposed Bayesian despeckling scheme is evaluated on both syntheticallyspeckled and real ultrasound images, and the results are compared to that of some other existing despeckling schemes. Modeling of Curvelet Coefficients The multiplicative degradation model of a speckle-corrupted ultrasound image g(i,j) in the spatial domain is given by g(i,j) = v(i,j)s(i,j)(1) where v(i,j) and s(i,j) denote the noise-free image and the speckle noise, respectively. This model of the noisy observation of v(i,j) can be additively decomposed as a noise-free signal component and a signal-dependant noise: g(i,j) = v(i,j) + (s(i,j) à ¢Ã‹â€ ’1)v(i,j) = v(i,j) + u(i,j)(2) where (s(i,j) à ¢Ã‹â€ ’1)v(i,j) represents the signal-dependant noise. Taking the curvelet transform of (2) at level l, we have y[l,d](i,j) = x[l,d](i,j) + n[l,d](i,j)(3) where y[l,d](i,j), x[l,d](i,j) and n[l,d](i,j) denote, respectively, the (i,j)th curvelet coefficient of the observed image, the corresponding noise free image and the corresponding additive signal-dependant noise at direction d= 1,2,3, ·Ãƒâ€šÃ‚ ·Ãƒâ€šÃ‚ ·,D. In order to simplify the notation, we will henceforth drop both the superscripts land dand the index (i,j). In this work, in order to reduce the noise inherited in ultrasound images, we propose exploiting the statistical characteristics of the curvelet coefficients in (3) to derive an efficient Bayesian estimator. Thus, one needs to provide a prior probabilistic model for the curvelet coefficients of xand n. It has been shown that the distribution of the curvelet coefficients of noise-free images can be suitably modeled by the Cauchy distribution [9]. The zero-mean Cauchy distribution is given by px(x) = (ÃŽÂ ³/à ₠¬)(x2 + ÃŽÂ ³2)(4) where ÃŽÂ ³is the dispersion parameter. The noisy observation is used to estimate the Cauchy distribution parameter ÃŽÂ ³by minimizing the function 2 Â à †Ë†yyt (t) à ¢Ã‹â€ ’à †(t) eà ¢Ã‹â€ ’ dt(5) where à †Ë†y(t) is the empirical characteristic function corresponding to the curvelet coefficients yof 22 the noisy observation, à †y(t) = à †x(t)à †E(t), à †x(t) = eà ¢Ã‹â€ ’Π³|t|, and à †E(t) = eà ¢Ã‹â€ ’(à ƒà ¯Ã‚ ¿Ã‚ ½/2)|t| deviation à ƒEobtained as with the standard à ƒE= MAD(y(i,j)) 0.6745 (6) In (6), MAD denotes the median absolute deviation operation. Now, in order to formulate the Bayesian estimator, a prior statistical assumption for the curvelet coefficients of nof the signal dependant noise should also be assumed. From experimental observation, it is noticed that the tail part of the empirical distribution of ndecays at a low rate. Hence, in this paper, we propose to use a two-sided exponential (TSE) distribution given by 1 pn(n) =eà ¢Ã‹â€ ’|n|/ÃŽÂ ² 2ÃŽÂ ² (7) where ÃŽÂ ²is a positive real constant referred to as the scale parameter. The method of log-cummulants (MoLC) is adopted to estimate the parameter ÃŽÂ ², and thus the estimated ÃŽÂ ²Ãƒâ€¹Ã…“ is obtained by using the following expression: ÃŽÂ ²Ãƒâ€¹Ã…“ = exp 1N1        N2 log(y(i,j))+ ÃŽÂ ¾ (8) N1N2 i=1j=1 where ÃŽÂ ¾is the Euler-Mascheroni constant and N1 and N2 defi ne the size N1 ÃÆ'-N2 of the curvelet subband considered. Bayesian Estimator Due to the fact that each of the Cauchy and TSE distributions has only one parameter, one could expect the process of Bayesian estimation to be of lower complexity. The values of the Bayes estimates xˆof the noise-free curvelet coefficients xof a subband under the quadratic loss function, which minimizes the mean square error (MSE), are given by the shrinkage function: xˆ(y) =px|y(x|y)xdx P pn(yà ¢Ã‹â€ ’x)px(x)xdx =P p(yà ¢Ã‹â€ ’x)p(x) (9) It is noted that a closed-form expression for xˆ(y) given by the above equation does not exist. Thus, in order to obtain the Bayesian estimates for the noise-free curvelet coefficients, the two integrations associated with (9) are numerically performed for each curvelet coefficient. Since this procedure requires an excessive computational effort, the bayseian estimates are obtained by replacing the associated integrals in (9) with infi nite series as suggested in [10]. Accordingly, the Bayesian shrinkage function can be expressed as eà ¢Ã‹â€ ’y/ÃŽÂ ²[f (y)ÃŽÂ ¶] + ey/ÃŽÂ ²[ f(y) + ÃŽÂ ¶] xˆ(y) =(10) eà ¢Ã‹â€ ’y/ÃŽÂ ²[f21(y) + ÃŽÂ ¶2] + ey/ÃŽÂ ²[à ¢Ã‹â€ ’f22(y) + ÃŽÂ ¶2] where f11(y) = f12 (à ¢Ã‹â€ ’y) = sin(ÃŽÂ ³/ÃŽÂ ²) Im E( à ¢Ã‹â€ ’y+ jÃŽÂ ³)à ¢Ã‹â€ ’Si(ÃŽÂ ³/ÃŽÂ ²) + à ₠¬ 1ÃŽÂ ²2 à ¢Ã‹â€ ’y+jÃŽÂ ³ à ¢Ã‹â€ ’cos(ÃŽÂ ³/ÃŽÂ ²)  Re E1(ÃŽÂ ² + Ci(ÃŽÂ ³/ÃŽÂ ²) ,(11) f(y) = à ¢Ã‹â€ ’f 1à ¢Ã‹â€ ’y+ jÃŽÂ ³ (à ¢Ã‹â€ ’y) = à ¢Ã‹â€ ’ sin(ÃŽÂ ³/ÃŽÂ ²) Re E()+ Ci(ÃŽÂ ³/ÃŽÂ ²) 2122ÃŽÂ ³1ÃŽÂ ² 1à ¢Ã‹â€ ’y+jÃŽÂ ³Ãƒ â‚ ¬ à ¢Ã‹â€ ’Π³cos(ÃŽÂ ³/ÃŽÂ ²)  Im E1(ÃŽÂ ² à ¢Ã‹â€ ’Si(ÃŽÂ ³/ÃŽÂ ²) + 2 ,(12) ÃŽÂ ¶1 = lim f12 yà ¢Ã¢â‚¬ ’à ¢Ã‹â€ Ã… ¾ (y) = sin(ÃŽÂ ³/ÃŽÂ ²) à ¢Ã‹â€ ’Si(ÃŽÂ ³/ÃŽÂ ²) + à ₠¬ à ¢Ã‹â€ ’cos(ÃŽÂ ³/ÃŽÂ ²)Ci(ÃŽÂ ³/ÃŽÂ ²), and(13) ÃŽÂ ¶= lim f 11 (y) =sin(ÃŽÂ ³/ÃŽÂ ²)Ci(ÃŽÂ ³/ÃŽÂ ²) +cos(ÃŽÂ ³/ÃŽÂ ²) à ₠¬ Si(ÃŽÂ ³/ÃŽÂ ²) + (14) 222 yà ¢Ã¢â‚¬ ’à ¢Ã‹â€ Ã… ¾ In the equations above, j= à ¢Ã‹â€ Ã… ¡Ãƒ ¢Ã‹â€ ’1, Im{ ·}and Re{ ·}are the imaginary and real parts, respectively, of a complex argument, and E1( ·), Si( ·) and Ci( ·) are, respectively, the exponential, sine and cosine integral functions obtained as in [10]. Experimental Results Extensive experimentations are carried out in order to study the performance of the proposed despeckling scheme. The results are compared with those of other existing despeckling schemes that use improved-Lee fi ltering [2], adaptive-wavelet shrinkage [6], and contourlet thresholding [7]. Performance evaluation of the various despeckling schemes is conducted on synthetically-speckled and real ultrasound images. In the implementation of the proposed speckling scheme, the 5-level decomposition of the curvelet transform is applied. From the experimental observation, applying a higher level of decomposition of the curvelet transform does not lead to any improvement in the despeckling performance. Since the curvelet transform is a shift-variant transform, the cycle spinning [11] is performed on the observed noisy image to avoid any possible pseudo-Gibbs artifacts in the neighborhood of discontinuities. In the proposed despeckling scheme, only the detail curvelet coefficients are despec kled using the Bayesian shrinkage function in (10). The peak signal-to-noise ratio (PSNR) is used as a quantitative measure to assess the despeckling performance of the various schemes when applied on synthetically-speckled images. Table I gives the PSNR values obtained when applying the various schemes on two synthetically-speckled images of size 512ÃÆ'-512, namely, Lenaand Boat. It is obviously seen from this table that, in all cases, the proposed despeckling scheme provides higher values of PSNR compared to that provided by the other schemes. To have a better insight on the despeckling performance of the various schemes, the results in Table 1 are visualized in Figure 1. It is obvious from this fi gure that the superiority of the proposed scheme over the other schemes is more evident when a higher level of speckle noise is introduced to the test images. In order to study the performances of the various despeckling schemes on real ultrasound images, two images obtained from [12] and shown in Figure 2 are used. Since the noise-fr ee images cannot be made available, one can only give a subjective evaluation of the performance of the various despeckling schemes. From Figure 2, it is clearly seen that the schemes in [2] and [6] provide despeckled images that suffer from the presence of visually noticeable speckle noise. On the other hand, the scheme in [7] severely over-smooth the noisy images thus providing despeckled images in which some of the texture details are lost. However, the proposed despeckling scheme results in images with not only a signifi cant reduction in the speckle noise but also a good preservation of the textures of the original images. Table 1: The PSNR values obtained when applying the various despeckling schemes on Lenaand Boatimages contaminated by speckle noise at different levels. 34 [2] 32[6] 30[7] Proposed 28 26 24 22 20 18 0.10.20.30.40.50.71 Standard deviation of noise (a) 32 [2] 30[6] 28[7] Proposed 26 24 22 20 18 16 0.10.20.30.40.50.71 Standard deviation of noise (b) Fig. 1: Quantitative comparison between the various despeckling schemes in terms of PSNR values: (a) Lenaimage; (b) Boatimage. Conclusion In this paper, a new curvelet-based scheme for suppressing the speckle noise in ultrasound images has been developed in the framework of Bayesian estimation. The observed ultrasound image is fi rst additively decomposed into noise-free and signal-dependant noise components. The Cauchy and twosided exponential distributions have been used as probabilistic models for the curvelet coefficients of the noise-free and signal-dependant noise components, respectively, of the ultrasound image. The proposed probabilistic models of the curvelet coefficients of an observed ultrasound image has been employed to formulate a Bayesian shrinkage function in order to obtain the estimates of the noise-free curvelet coefficients. A low-complexity realization of this shrinkage function has been employed. Experiments have been carried out on both synthetically-speckled and real ultrasound images in order to demonstrate the performance of the proposed despeckling scheme. In comparison with some other ex isting despeckling schemes, the results have shown that the proposed scheme provides higher PSNR values and gives well-despeckled images with better diagnostic details. (b) (c)(d)(e)(f) (g)(h)(i)(j) Fig. 2: Qualitative comparison between the various despeckling schemes. (a)(b) Noisy ultrasound images. Despeckled images obtained by applying the schemes in (c)(g) [2] ,(d)(h) [6] ,(e)(i) [7] and (f)(j) the proposed scheme. References Dhawan, A.P.: Medical image analysis. Volume 31. John Wiley Sons (2011) Loupas, T., McDicken, W., Allan, P.:  An adaptive weighted median fi lter for speckle suppression in medical ultrasonic images. IEEE transactions on Circuits and Systems 36(1) (1989) 129-135 Coup ´e, P., Hellier, P., Kervrann, C., Barillot, C.: Nonlocal means-based speckle fi ltering for ultrasound images. IEEE transactions on image processing 18(10) (2009) 2221-2229 Sridhar, B., Reddy, K., Prasad, A.: An unsupervisory qualitative image enhancement using adaptive morphological bilateral fi lter for medical images. International Journal of Computer Applications 10(2i) (2014) 1 Abd-Elmoniem, K.Z., Youssef, A.B., Kadah, Y.M.: Real-time speckle reduction and coherence enhancement in ultrasound imaging via nonlinear anisotropic diffusion. IEEE Transactions on Biomedical Engineering 49(9) (2002) 997-1014 Swamy, M., Bhuiyan, M., Ahmad, M.: Spatially adaptive thresholding in wavelet domain for despeckling of ultrasound images. IET Image Process 3(3) (2009) 147-162 Hiremath, P., Akkasaligar, P.T., Badiger, S.: Speckle reducing contourlet transform for medical ultrasound images. Int J Compt Inf Engg 4(4) (2010) 284-291 Jian, Z., Yu, Z., Yu, L., Rao, B., Chen, Z., Tromberg, B.J.: Speckle attenuation in optical coherence tomography by curvelet shrinkage. Optics letters 34(10) (2009) 1516-1518 Deng, C., Wang, S., Sun, H., Cao, H.: Multiplicative spread spectrum watermarks detection performance analysis in curvelet domain. In: 2009 International Conference on E-Business and Information System Security. (2009) Damseh, R.R., Ahmad, M.O.: A low-complexity mmse bayesian estimator for suppression of speckle in sar images. In: Circuits and Systems (ISCAS), 2016 IEEE International Symposium on, IEEE (2016) 1002-1005 Temizel, A., Vlachos, T., Visioprime, W.: Wavelet domain image resolution enhancement using cycle-spinning. Electronics Letters 41(3) (2005) 119-121 Siemens  Healthineers: https://www.healthcare.siemens.com/ultrasound. Accessed:  2017-01-06.
Friday, January 17, 2020
Slave Trade Simulation
Slave Trade Simulation Paper 26 February 2013 Modern World ONL Trading slaves, a practice that has been described as inhumane, evil, or even blasphemous, left little room for sensitivity for those making the decisions of the trade. Often people wonder how such evil could continue in the world for as long as it did. â€Å"The rewards of the slave trade overwhelmed any religious inhibitions that some of the traders and other beneficiaries might have had. 1 [Islam’s Black Slaves, p. 159] I will explain the delicacies of the trade agreements of the Yao, Kilwa-based Swahili Trade Lineage, and of the Zanzibari Indian Trading Lineage. After taking control of Kilwa in the mid-1780s, Oman transferred the bulk of the slave and ivory trade there. 2 [Islam’s Black Slaves, p. 146] The Swahili Trading Lineage of Kilwa were pleased, as it leveled the playing field and enabled trade to be profitable for everyone of the area.If you are wondering how these slaves could have put up with such harsh conditions, keep in mind that, though the traders poorly treated slaves, the slave-owners often treated them more humanely. The ones who were not killed in the travel of the trade were lucky to be alive and thus weakened at the thought of revolt. As for the traders, many of their negotiations were so binding that they could not go back on an agreement at any cost. Trading elite were normally more concerned with upholding status as businessmen and thus, carried out any horror in the name of honor.The politics of the slave trade were very much like those of the 21st century, in the sense that some were at the tip of the pyramid, with those who were the middle and finally its base. Yao elite kept their honor and held their position on the ground level by providing for their people through trade. Their mercantile success also determined their power locally, as they were a matrilineal society. The Swahili Trading Lineage (A. K. A. the next level of the pyramid), who acquired t heir slaves from the Yao, were facing pressures from the Zanzibari Indian Traders, who were controlling more and more of their territory.These Zanzibari, who were actually Bhattians based in Oman, were looking to prove their worth with the Omani by influencing more trade in the Kilwa region, thus moving up a notch on the pyramid. The Omani (the eye of the pyramid) had recently forced the Portuguese out of power over their area, allowing for more even trade for the Swahili Trading Lineage. All were trying to hold their ground against the exerting power of the Omani while remaining in good relations with those who benefited them.The Yao headmen, who were having trouble keeping their slaves alive prior to the trade, had to make a decision as to where 50 additional slaves to be given to the Swahili Lineage would come from. They could have chosen to attack a neighboring village, which might have had devastating consequences. Second, they could have offered up criminals of local villages. This would run them the risk of upsetting powerful families and causing half of the headmen’s lives if expected profits were not realized.Third, they had the option of sending Yao traders to the Portuguese for the extra slaves. This would have caused prices to increase by 50%, which would have affected their probability of providing the right amount of slaves. For the Kilwa-based Swahili Trading Lineage, their main concern was conducting an effective and honorable business transaction in order to promote a marriage alliance with the Zanzibari Indian Trading Lineage. This would counter the growing authority the Zanzibari had over the Kilwas and protect their status as elites.In order to do so, they had to fairly treat the Yao traders while ensuring a profit of at least 10 slaves and a gift for the Zanzibari of at least 10 slaves as well. Possibly the most influential of the transaction were the Zanzibari Indian traders. They set the market prices and held the fate of the Kilw a-based lineage in their hands. For the trade, wealth was just as important as power for the few who conducted the human trade. Profits were estimated to be over 60 percent, substantially higher for anyone who simultaneously traded ivory. Traders were not inclined to let go of their influence at any cost. Those who stood in the way of a successful trade were eradicated or assimilated. Bibliography Ronald Segal,  Islam's Black Slaves: The Other Black Diaspora ( New York: Farrar, Straus and Giroux, 2002) Robert E. Strayer,  Ways of the World: A Brief Global History, Volume II, Since 1500 (New York: Bedford/St. Martin's, 2012) â€â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€œ [ 1 ]. Ronald Segal,  Islam's Black Slaves: The Other Black Diaspora ( New York: Farrar, Straus and Giroux, 2002)
Thursday, January 9, 2020
Analyzing Twain´s The Man that Corrupted Hadleyburg and...
An enigmatic person strolls into a humble village secluded in the mountains, ignorant to many things. The enigma then enlightens the villagers to the truth whether good or bad. Mark Twain uses such a scenario in many of his works such as The Man that Corrupted Hadleyburg, and The Mysterious Stranger. In both stories are set in small towns whos residents are oblivious to their own moral hypocrisy. The sudden appearance of a stranger spreading a sort of knowledge, initiates a chain of events the leads to certain residents to self-evaluate their own character and that of the whole human race. Its is through these Mysterious Strangers and the events they trigger that Twain is able to depict his unfiltered cynical view of the moral status†¦show more content†¦Twain through Satan claims that Mans ability to distinguish between right and wrong along with the free will to choose between them inadvertently creates the possibility of wrongdoing, as right and wrong are just constructs made to justify mans actions (Stranger 78). At a later point in the novel Twain shows that man applies their morals in a herd like mentality. Satan and Theodor witness a lynching, Theodore joins in stoning the victim, and Satan laughs at the fact that while the entire mob participated in the stoning sixty-two hypocrites had no wish to throw a stone, only doing so in order to avoid persecution from the rest of the mob (Twain Stranger 109). This shows the idea that while a man may be preaching something that is morally wrong yet will still have followers as long as they are vocal about it. At one point eleven girls are put to stake for witchcraft which Theodore concluded was a just and right cause yet when the girls screams got to loud he left as it was too dreadful(Twain Stranger 82). Satan spends much of his time with Theodor admonishing the human race for their acquisition of the Moral Sense and boasting his superiority for the lack of it. Contradicting Satan, Ronald Gervais who a rgues that the Moral Sense
Wednesday, January 1, 2020
World War One Essay - 4745 Words
Question and answer format of everything in world war 1 1. Machine guns: These weapons were first used in the American Civil War to devastating effect. But with World War One their effectiveness reached frightening new levels. Firing up to 600 bullets a minute (the equivalent of 250 men with rifles), Machine Guns were then deemed to be ‘weapons of mass destruction. Machine guns would often be grouped together to maintain a constant defensive position. 2. Artillery: These were the new and upgraded versions of cannons. Never in the history of man, where there so many cannons used in one war alone. For four years the British had been using artillery and firing 170 million shells in that time. But Germany had a plan up their sleeve. For†¦show more content†¦The basic idea of a flamethrower is to spread fire by launching burning fuel. The earliest flamethrowers date as far back as the 5th century B.C. These took the form of lengthy tubes filled with burning solids (such as coal or sulphur), and which were used in the same way as blow-guns: by blowing into one end of the tube the solid material inside would be propelled towards the operators enemies. 11. Trench Mortars: As with the grenade the mortar was yet another old weapon which found a new lease of life during World War One. A mortar is essentially a short, stumpy tube designed to fire a projectile at a steep angle (by definition higher than 45 degrees) so that it falls straight down on the enemy. From this simple description it will be immediately apparent that the mortar was ideally suited for trench warfare, hence the common application of the trench prefix. 2. What is no mans land: No Mans Land is the term used by soldiers to describe the ground between the two opposing trenches. Its width along the Western Front could vary a great deal. The average distance in most sectors was about 250 yards (230 metres). 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The Allies, initially consisting of Englan d, France, and Russia, fought against the Central Powers of Germany and Austria-Hungary;Read MoreEssay on World War One1910 Words  | 8 PagesWorld War One When the guns of August 1914 shattered the peace of Europe, pitting Germany and Austria-Hungary (the Central Powers) against Britain, France, and Russia, President Woodrow Wilson on August 4 issued a proclamation of neutrality. Two weeks later he urged Americans to be impartial in thought as well as in action. But in the realms of both official policy and public opinion, neutrality proved difficult to sustain. Wilson insisted, for reasons of both principle and economic advantageRead MoreThe Events Of World War One1108 Words  | 5 PagesWorld War One, a war that was centralized in Europe and resulted due to the reaction to the assassination of the archduke of Austria-Hungary, Franz, Ferdinand, and his wife, Sophia, on June 28th, 14 at Sarajevo, Bosnia-Herzegovina’s at the hands of Gavrilo Princip, a nineteen year old who had ties with the military group known as the Black Hand. For the war, there were two alliances made that were fighting: the Triple Entente made up of Br itain, France, and Russia and the Triple Alliance made upRead MoreThe Progression of World War One Essay599 Words  | 3 PagesWorld War I began in July 1914. World War I is also known as The Great War and The First World War. Many Americans were for the Allies and hoped for their victory, but did not want to join the conflict. Woodrow Wilson was the President of the United States when the war began. The assassination of Archduke Franz Ferdinand and his wife Sophie started the war. Ferdinand was assassinated by Gavrilo Princip, in Sarajevo, Bosnia on June 28, 1914. On July 28, Austria-Hungary declared war on Germany. OnRead MoreEssay on Causes of World War One612 Words  | 3 PagesCauses of World War One There were many reasons as to why WWI began. The long term causes were imperial and economic rivalry between Britain and Germany. Britain was jealous of Germany because Germanys strength was growing- it was producing more iron and steel than Britain. Germanys navy was growing and its empire was expanding. Germany was jealous of Britains empire and wealth. It also feared the fact that unfriendly countries (France and Russia) were on its eastern
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