How Good People Make Tough Choices Essay Example | Topics and Well Written Essays - 2000 words

How Good People Make Tough Choices - Essay Example Good people and tough choices are two central themes within this book. The author tries to identify good people by quoting their several daily life examples and their personal and social experiences and their traits as well. At the same time, the author endeavors to explain tough choices. In this regard, it is important to highlight that the author has separately explained â€Å"choices† and â€Å"tough choices† from the prism of ethics. For example, in defining the personality traits of good people, the author has identified them as visionary, having ethical values, courageous enough to face the repercussions of their choices (Kidder 1). Similarly, the author has particularly emphasized on â€Å"choices† as they have broad common sense and they are commonly made by common people. By defining these choices, the author is trying to relate them with the ethical values and leadership qualities as well. Through this association, the author has been successful in maki ng and establishing relationship between ethical choices within the context of ethical values. Here, it is pertinent to highlight that the author has used several daily life examples for supporting the assertions mentioned in the book.   For example, in the first chapter, the author has compared the interplay between the ethics of right versus right in which he has highlighted that two ethically correct positions collide and the related individuals have to make one choice and one compromise; this is a common daily routine situation normally faced by all us.

Fieldtrip - King Arthur's Cave and Wye Valley Catchment Assignment

Fieldtrip - King Arthur's Cave and Wye Valley Catchment (Herefordshire) - Assignment Example It is important to highlight that the cave is located at the bottom of the low lying cliff that is at the mouth of the Lord Woods in the north eastern end in Doward, this is next to the Symonds Yat with a distance approximated to be 4 miles. It is known that the cave is said to have been inhabited by the early man in the upper Paleolithic era. The evidences that vindicate this are the archaeological exhibit that is the flint tools and the bones of the wooly mammoth. (Arthur, 2007). It is important to highlight that King Arthur’s cave is made of two chambers that intersect at the point of entrance. This is estimated to lie 300 feet above river Wye. It is important o note that one of the chambers is 25 feet in diameter and is circular in shape. The entrance also has a hearth that the archaeologists say was in existence for the past 12000 years. The Mesolithic artifacts have also been found in the cave. The chambers were called Bear’s den and Lion’s cave. This was because of the archaeological evidences that were confiscated in there. (John 2000). It is important to note that among the items that were found in the cave, the following were the ones that gave the evidence of the Mesolithic inhabitance as well the upper Paleolithic inhabitance. The bones were; wooly rhinoceros, cave bear and the hyena. The foreman of the elephant too was found in the cave. The people therefore spend much in the various areas that they spend their time in the name of visiting the place and this therefore earns foreign exchange to the country in question. The money that is earned from this is therefore use in the developments of the various infrastructural facilities that help in the boosting of the various economic sectors (John 2001). It is as well important to note the point that the Herefordshire is at the point of interconnection and at a point that it joins the Wye River. The place therefore has an added advantage of the valley is the nearby forest also acts

Choices Essay Example for Free

Choices Essay The lives of people have always been filled numerous dilemmas. The choices that we make sometime in our lives could either make or break an individual. As a child, I have always dreamt of making it big in the world that I live in. In order to fulfill my dreams, I decided to pursue a Masters Degree in Education from the Cambridge College. Being given such opportunity would allow me to hone my craft and share my knowledge with others. From the many experiences that I have encountered in my life, being given the chance to live my life for the second time around was the most important. Unknown to many, I am a cancer survivor. I was diagnosed with the said disease at the age of eighteen. Regardless of such trials, my determination to succeed in life was never tarnished. I wanted to prove something to myself that I had what it takes to be someone in society regardless of all the impediments that came my way. Learning is an essential part of my growth, both as an individual and as a professional. In order to be successful, I have to personify the different techniques taught in school. Furthermore, I may also incorporate my personal thoughts and experiences in trying to make things work. Such actions would not only focus on the theoretical aspects of learning, but also in the application process. Then I could definitely say that I am able fully comprehend everything that have been inculcated in me by the school and my personal experiences. Aside from acquiring a Master’s Degree, I still have numerous goals in mind. I want to experience working with young minority men, especially those who were in middle school. This was in support of my desire to work within the public school system. I wanted to be of assistance to students, especially those who were of low social and economic statuses. This drive may also be attributed to the fact that studies have been conducted showing that young black male students normally lose interest in going to school when they reach the sixth grade. Thus, the percentage of children dropping out from school increases each year. Furthermore, I have made advanced researches stating that the students of Richmond County are giving the teachers difficulty in keeping them in school. Motivation amongst teachers to the students was relatively difficult, for barriers have already been by the students. The situation is alarming for both the state and the educational system that the schools offer. This then leads to the difficulty of students in trying to overcome the challenges that come their way. They are emotionally battered, and are in search of people whom they can cling to, such as guidance counselors. Although difficult, I would like to be part of the lives of these children. Equipped with the necessary knowledge and training, I may impart to these students pertinent information that may inspire them to become successful in their lives. I hope that my little words of wisdom would also serve as tools that would help them survive their lifetime. I believe that I have what it takes to be accepted in your institution. My experiences were focused on the relationship of people with each other. The counseling experiences I had were very helpful, for I witnessed how it was to deal with pregnant teenagers. This stage of their lives was a very crucial one, for several ideas and emotions are heightened. Sometimes, they would pity themselves for what happened, or worse, attempt suicide. I may be able to utilize my social counseling experiences and adopt them to some of the techniques I can use in counseling students in the educational field. This may sound easy, but it would also entail hard work and determination from my end. My dreams do not end after acquiring my degree from your institution. I would utilize my degree in working within the school system in order to facilitate an outreach program for both the students and the parents. The school districts within our area are also in the initial stage of discussing the requirements deemed by the students, as mentioned earlier. School counselors would be required by the Superintendent for each school, and this would come to my advantage for I may have my degree by that time. Furthermore, I also intend to teach college level courses. Learning is a continuous process that also helps people to improve their craft. Having a Master’s Degree would not hinder me from moving forward. One of my goals is to obtain a Doctoral Degree in Counseling. The degree would be beneficial in my growth as a counselor, for my scope of knowledge would be enhanced further. In addition to this, the courses offered would allow me to learn new approaches that I may use towards students. Although my quest for knowledge is unstoppable, I believe that there are certain things that I should still care about. I am like any other applicant; the only difference seen was the fact that I am more inclined in fulfilling my goals in life. Working and being exposed to different kinds of people help me to decipher the different approaches that should be used on people. The knowledge acquired from the institution would come as an advantage, for the personalities of people have been studied and researched by people who specialize in the said field. I, too, am guilty of wanting to improve each action made, especially when it comes to counseling students. I know that being accepted in your institution requires a big deal of commitment from the students. The training would be demanding and challenging, yet ultimately rewarding. The little mistakes that are often committed would be given much importance, and improvements would definitely be visible. I aim to learn and grow, both as an individual and as a professional. I know that this institution would bring me a step closer to my dreams and aspirations. The choice I made was evident – your institution would help me be the best individual that I can be.

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€ Ã¢â‚¬â„¢1)v(i,j) = v(i,j) + u(i,j)(2) where (s(i,j) à ¢Ã‹â€ Ã¢â‚¬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 Ã¢â€š ¬)(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   Ãƒ Ã¢â‚¬  Ãƒâ€¹Ã¢â‚¬  yyt (t) à ¢Ã‹â€ Ã¢â‚¬â„¢Ãƒ Ã¢â‚¬  (t) eà ¢Ã‹â€ Ã¢â‚¬â„¢ dt(5) where à Ã¢â‚¬  Ãƒâ€¹Ã¢â‚¬  y(t) is the empirical characteristic function corresponding to the curvelet coefficients yof 22 the noisy observation, à Ã¢â‚¬  y(t) = à Ã¢â‚¬  x(t)à Ã¢â‚¬  E(t), à Ã¢â‚¬  x(t) = eà ¢Ã‹â€ Ã¢â‚¬â„¢ÃƒÅ½Ã‚ ³|t|, and à Ã¢â‚¬  E(t) = eà ¢Ã‹â€ Ã¢â‚¬â„¢(à Ã†â€™Ãƒ ¯Ã‚ ¿Ã‚ ½/2)|t| deviation à Ã†â€™Eobtained as with the standard à Ã†â€™E= 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€ Ã¢â‚¬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  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚   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 xˆ  of 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€ Ã¢â‚¬â„¢x)px(x)xdx =P p(yà ¢Ã‹â€ Ã¢â‚¬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€ Ã¢â‚¬â„¢y/ÃŽÂ ²[f (y)ÃŽÂ ¶] + ey/ÃŽÂ ²[ f(y) + ÃŽÂ ¶] xˆ (y) =(10) eà ¢Ã‹â€ Ã¢â‚¬â„¢y/ÃŽÂ ²[f21(y) + ÃŽÂ ¶2] + ey/ÃŽÂ ²[à ¢Ã‹â€ Ã¢â‚¬â„¢f22(y) + ÃŽÂ ¶2] where f11(y) = f12 (à ¢Ã‹â€ Ã¢â‚¬â„¢y) = sin(ÃŽÂ ³/ÃŽÂ ²) Im E( à ¢Ã‹â€ Ã¢â‚¬â„¢y+ jÃŽÂ ³)à ¢Ã‹â€ Ã¢â‚¬â„¢Si(ÃŽÂ ³/ÃŽÂ ²) + à Ã¢â€š ¬ 1ÃŽÂ ²2 à ¢Ã‹â€ Ã¢â‚¬â„¢y+jÃŽÂ ³ à ¢Ã‹â€ Ã¢â‚¬â„¢cos(ÃŽÂ ³/ÃŽÂ ²)   Re   E1(ÃŽÂ ² + Ci(ÃŽÂ ³/ÃŽÂ ²) ,(11) f(y) = à ¢Ã‹â€ Ã¢â‚¬â„¢f 1à ¢Ã‹â€ Ã¢â‚¬â„¢y+ jÃŽÂ ³ (à ¢Ã‹â€ Ã¢â‚¬â„¢y) = à ¢Ã‹â€ Ã¢â‚¬â„¢ sin(ÃŽÂ ³/ÃŽÂ ²) Re E()+ Ci(ÃŽÂ ³/ÃŽÂ ²) 2122ÃŽÂ ³1ÃŽÂ ² 1à ¢Ã‹â€ Ã¢â‚¬â„¢y+jÃŽÂ ³Ãƒ Ã¢â€š ¬ à ¢Ã‹â€ Ã¢â‚¬â„¢ÃƒÅ½Ã‚ ³cos(ÃŽÂ ³/ÃŽÂ ²)   Im   E1(ÃŽÂ ² à ¢Ã‹â€ Ã¢â‚¬â„¢Si(ÃŽÂ ³/ÃŽÂ ²) + 2 ,(12) ÃŽÂ ¶1 = lim f12 yà ¢Ã¢â‚¬  Ã¢â‚¬â„¢Ãƒ ¢Ã‹â€ Ã… ¾ (y) = sin(ÃŽÂ ³/ÃŽÂ ²) à ¢Ã‹â€ Ã¢â‚¬â„¢Si(ÃŽÂ ³/ÃŽÂ ²) + à Ã¢â€š ¬ à ¢Ã‹â€ Ã¢â‚¬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 Ã¢â€š ¬ Si(ÃŽÂ ³/ÃŽÂ ²) + (14) 222 yà ¢Ã¢â‚¬  Ã¢â‚¬â„¢Ãƒ ¢Ã‹â€ Ã… ¾ In the equations above, j= à ¢Ã‹â€ Ã… ¡Ãƒ ¢Ã‹â€ Ã¢â‚¬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.

The Brazil Nut (Bertholletia excelsa) :: Botany

The Brazil Nut (Bertholletia excelsa) The Brazil Nut is the fruit of a tree that grows mostly wild in rainforests. Castanheiro do Para, which is the Brazilian name given to this tree, is found in many Amazonian states of Brazil, Peru, Columbia, Venezuela and Ecudor. It is most pervalent in the Brazilian states of Marahao, Mato Grosso, Acre, Para, Rondonia, and the Amazonas. The tree is enormous, Frequently attaining the height of 160 feet or more. The fruit is a large spherical woody capsule or pod and measures an average of six inches in diameter and can weigh up to 5 pounds. The fruit pods grow at the ends of thick branches, then ripens and falls from the tree from January to June. Inside each fruit pod is 12 to 25 Brazil nuts with their own indvidual shell(1). Brazil nuts are harvested at plantations and in the wild. Plantations are being developed in various parts of the Amazon. Fazenda Aruana is the owner of a 12,000 hectare former cattle ranch, partially converted to a Brazil Nut plantation in 1980. By January of 1990, 318,660 Brazil nut trees were planted on 3341 hectares of land. Fazenda's original intent was to plant Brazil Nut trees in a 20 by 20 meter grids and allow cattle grazing between the trees. The trees in the Aruana plantation are the result of grafting high yield clones from the region of Abufari Amazonas were Brazil nuts are know for their large fruits and seeds. As a result of fertilization from the same clones, the fruit production among clones has been low(2). Another danger in using so few clones is the ability to resist attack of disease and insects. The bulk of the Brazil nuts that are harvested are done so in the wild. They are harvested during a five to six month period in the rainy season. The fruits, witch weigh from .5 to 2.5 kilograms and contain ten to twenty five seeds, are gathered immediately after they fall. This minimizes the chance of insect or fungal attack on seeds. Brazil nuts are also carried away by animals. The number of pods can range form 63 to 216 per tree(). Most of the pods gathered in the wild are sent down river to processing plants were they are opened out of the pod and packaged. The brazil nut has a major impact on local Amazonian economies. The numbers on total production are estimates due to the fact figures are hard to get from the Amazon.

term limits in congress :: essays research papers

THESIS:  Ã‚  Ã‚  Ã‚  Ã‚  Term limits for Congress will disrupt the balance and can make the taxpayers very unhappy. SUPPORTING DETAILS: 1)  Ã‚  Ã‚  Ã‚  Ã‚  Too many new, inexperienced members can hurt voters, as rookie legislators find it hard to navigate the bureaucracy. 2)  Ã‚  Ã‚  Ã‚  Ã‚  Term limits will force out well respected politicians. 3)  Ã‚  Ã‚  Ã‚  Ã‚  It will take away the voters’ right to choose their politicians. 4)  Ã‚  Ã‚  Ã‚  Ã‚  Long term politicians will have â€Å"good behavior† in order to ensure their reelection. 5)  Ã‚  Ã‚  Ã‚  Ã‚  The more experienced the politician, the better they will handle the peoples’ affairs. There were many things that I had to go through to complete this assignment. As with anything that you do, you will need to work hard to overcome obstacles, some which may be more difficult than others. Everyone has their own way of dealing with dilemmas. This assignment was a definite dilemma for me. Politics is by far the worst subject for me. I tend to get very lost when it comes to this. This is definitely an area I need to work on. I had to read many articles in order to form an opinion on this matter. Even after all the reading I still had trouble deciding on a side to take. I had to question myself many times to get the right take on it for myself. It was difficult but it seems that I have managed to come up with a good argument. Questioning is a great strategy for me to use. This has helped me out very much in this particular assignment. The argument could have really gone either way, but questioning myself helped make the decision much easier. Brainstorming or listing is another helpful tool that everyone should use. This has helped me form my supporting details. It is easier to sit down and rack your brain and list all the points than it is to just free-write all your ideas. Free-writing can sometimes be a jumbled mess.

Physical Networking Essay

When installing a cabling system there are a number of factors that come into play. Choosing the appropriate LAN device, cost of the whole job which is a major factor, and device interconnections. Knowing your codes are very important as well. When it comes to choosing the appropriate LAN device this is extremely important. Choosing the right router plays a part, some routers only work with certain types of operation systems. Choosing the right switch is important. You would want to choose a switch that has a mixture of both UTP and fiber ports. People sometimes routers depending on their price or speed. Cost are determined by the type of LAN and WAN networks you are trying to setup when setting up a network you need to consider 4 physical areas. Work area, telecommunications room, backbone cabling, and distribution cabling. Work areas are locations devoted to end devices used by individual users. Telecommunications room is where connection to intermediary devices take place. Horizontal cabling connects the telecommunications rooms with the work areas. While backbone cabling is used to connect the telecommunications rooms to the equipment rooms, where the servers are often located. If you don’t know the fire codes when it comes to wiring you shouldn’t be installing or attempting to even make wires for the network you are developing. (Cabling and Planning Networks, 2015) Works Cited Cabling and Planning Networks. (2015, January 25). Retrieved from High Tech : http://www.hightech.net