Activities for Nonverbal Communication

Activities for Nonverbal Communication Have you ever made an instant judgment about a person, without ever speaking to him or her? Can you tell when other people are worried, afraid, or angry? We can sometimes do this because we are tuning in to nonverbal clues. Through nonverbal communication, we make all kinds of inferences and decisions- often without realizing it. It’s important to be aware of nonverbal communication, so we can avoid sending and receiving unintentional messages through our expressions and body movements. These exercises are designed to help you understand how much information we transmit through nonverbal communication. Nonverbal Activity 1: Wordless Acting Separate students into groups of two.One student in each group will perform the role of Student A, and one will perform as Student B.Give each student a copy of the script below.Student A will read his/her lines out loud, but student B will communicate his/her lines in a nonverbal manner.Provide student B with a secret emotional distraction that is written on a piece of paper. For example, student B may be in a rush, may be really bored, or may be feeling guilty.After the dialogue, ask each student A to guess what emotion was affecting their partner, student B. Dialogue: Student A: Have you seen my book? I can’t remember where I put it.Student B: Which one?Student A: The murder mystery. The one you borrowed.Student B: Is this it?Student A: No. It’s the one you borrowed.Student B. I did not!Student A: Maybe it’s under the chair. Can you look?Student B: OKjust give me a minute.Student A: How long are you going to be?Student B: Geez, why so impatient? I hate when you get bossy.Student A: Forget it. I’ll find it myself.Student B: Wait- I found it! Nonverbal Activity 2: We Have to Move Now! Cut several strips of paper.On each strip of paper, write down a mood or a disposition like guilty, happy, suspicious, paranoid, insulted, or insecure.Fold the strips of paper and put them into a bowl. They will be used as prompts.Have each student take a prompt from the bowl and read the sentence: We all need to gather our possessions and move to another building as soon as possible! expressing the mood they’ve selected.After each student has read their sentence, the other students should guess the emotion of the reader. Each student should write down assumptions they made about each speaking student as they read their prompts. Nonverbal Activity 3: Stack the Deck For this exercise, you will need a regular pack of playing cards and a lot of space to move around. Blindfolds are optional, and the task takes a bit longer if blindfolds are used. Shuffle the deck of cards thoroughly and walk around the room to give each student a card.Instruct the students to keep their card a secret. No one can see the type or color of anothers card.Make it clear to students that they will not be able to speak during this exercise.Instruct students to assemble into 4 groups according to suits (hearts, clubs, diamonds, spades) using nonverbal communication.Its fun to blindfold every student during this exercise (but this version is much more time consuming).Once students get into their groups, they must line up in order of rank, from ace to king.The group that lines up in correct order first wins! Nonverbal Activity 4: Silent Movie Divide students into two or more groups. For the first half of the class, some students will be screenwriters and other students will be actors. Roles will switch for the second half. The screenwriter students will write a silent movie scene, with the following directions in mind: Silent movies tell a story without words. It is important to start the scene with a person doing an obvious task, like cleaning the house or rowing a boat.This scene is interrupted when a second actor (or several actors) enters the scene. The appearance of the new actor/s has a big impact. Remember that the new characters could be animals, burglars, children, salesmen, etc.A physical commotion takes place.The problem is resolved.The acting groups will perform the script(s) while the rest of the class sits back and enjoys the show. Popcorn is a good addition to this activity.After each silent movie, the audience should guess the story, including the conflict and resolution. This exercise gives students a great opportunity to act out and read nonverbal messages.

JUST GETTING STARTED

JUST GETTING STARTED Put down that pen! Dont you even think about starting off your writing career with a book. Why? Because you havent become a writer yet. In all my conference classes, the first thing I tell people is this . . . BECOME A WRITER FIRST. A lot of people skip that part of the career the educational process, the word order, the flow, the friggin VOICE that so many people ignore. Thats why so much writing just muddies together. One big mass of earthworms, tangled together in a Kindle world. Just finished reading a short book about a ghost in Savannah. The writer has great potential. Shes even educated. Some comment she made on a blog made me buy the book. (Let THAT be another lesson for you.) However, she head hopped. She switched characters, disassociating pronouns. She skipped descriptions. It was so obvious that the material was in her head, but somewhere between her gray matter and her fingers, it got waylaid. So now, I associate her name with less-than-stellar writing. Premature writing is like a bad marriage. Its always in the background. Always a reminder. Sure, after enough years its remembered less, but seriously . . . do you want to wait years for a bad piece of work to fade away? Magazines. Start with magazines. No matter what you write, there are magazines you can write for. Yes, youll be rejected, as you should. Yes, youll get frustrating coming up with unique ideas, as you should. But magazine writing is the jumping off point for all writers, in my opinion. That and/or newspapers. No, Im not belittling either profession, but your opportunities are more and you learn lessons faster when you have to meet deadlines, or have to eat from the articles you sell. Romance, mystery, sci-fi, nonfiction, all exist. But write about anything . . . everything. Test yourself. If you think you have the guts, um, prowess, to write anything book-length, then magazine articles should not be daunting. Reasons to start with magazine writing: You learn how to carefully choose your words in a small space. (i.e., writing tight) YOu learn how to adhere to an editors needs. You learn how to write faster. You learn how to research, and cull that research since youll never need it all. You earn money, always a good thing. You earn clips, which actually show that . . . you are a writer. Tough love time. If an editor sees your

HALLIBURTON, organizational problems Essay Example | Topics and Well Written Essays - 3750 words

HALLIBURTON, organizational problems - Essay Example Success depended on its ability to deliver on what it charged and this had impressed the United States Military and its other clients. No one appeared to mind Halliburton overcharging if the work that it delivered could keep the troops happy when they were far from home. However, within the recent past allegations of unethical conduct, overcharging, kickbacks and political influence peddling have marred the image of this company which still wins awards for performance in its industry. This essay presents a discussion about the ethical and organizational problems that confront Halliburton Energy Services. Halliburton Energy Services is a multinational corporation with operations in 70 countries of the world (NationMaster.com, 2005, â€Å"Halliburton Energy Services†). The group provides technical products and services for oil and gas production and exploration. Revenues of Halliburton Energy Services were in excess of US$ 15 billion in 2007 and it employs nearly 51,000 people worldwide (Halliburton, 2007, pp. 2 – 5). In 2007, its revenue grew by 18 % year-over-year and about 50 % of the total revenue was from outside North America. More than 100 nationalities work with Halliburton Energy Services Inc with most of the employees working in their home countries. This firm has an old history and it began operations in 1919 when Mr. and Mrs. Erle P. Halliburton started the firm and found work cementing oil wells in Burkburnett, Texas. The company was later to move to Ardmore, Oklahoma and then to Duncan, Oklahoma before becoming listed on the New York Stock Exchange in 1948. The major spheres of activities for Halliburton involve providing technical products and services for oil and gas exploration and production, handled by its Energy Services Group. However, Halliburton’s major subsidiary KBR, or Brown & Roots was a major construction company of refineries, oil fields, pipelines, and chemical plants (Briody,

A report in the context of the audit of public companies listed on the Essay

A report in the context of the audit of public companies listed on the London Stock Exchange regarding the two issues that the committee decided against implementing - Essay Example auditor’s bringing a fresh perspective and greater skeptism that would be lacking in the long-standing auditor-client relationship, the opponents maintain that because the auditor’s lack of familiarity with the industry and client, audit quality would suffer under such a regime (AICPA 1992). In late 2001, the Enron debacle followed by its high-profile collapse now focuses attention on the profession’s effectiveness in protecting the interests of the public. Thus, Sarbanes-Oxley Act 2002 mandated the General Accounting Office (GAO) to conduct a research on the potential effects of mandatory audit rotation as required by law. The study concluded that mandatory audit rotation would not necessarily strengthen auditor independence (G.A.O. 2003). The arguments for and against mandatory audit firm rotation contend whether the auditing firm’s long-term client-customer relationship and the profitable desire to maintain the client adversely affects the public accounting firm’s independence during the auditing of a company’s financial statements. Furthermore, reservations about the likely effects of the audit firm rotation include the fear of losing company-specific information gathered by an audit firm over years of experience as an auditor, and whether the intended benefits are likely to outweigh the costs. Additionally, the implementation of the Sarbanes-Oxley Act (as applied in the United States) has raised question as to its effectiveness of reforming the intended benefits of mandatory audit firm rotation. Furthermore, research studies and other publications specifically show that the advantages and disadvantages of mandatory audit firm rotation touch on auditor independence, audit quality, and increased costs. Interference with auditor independence or audit quality can result in failure and adversely affect the parties relying on the fair representation of the financial statements in conformity with established accounting standards. Proponents of audit

Fuzzy Logic Technique for Image Enhancement

Fuzzy Logic Technique for Image Enhancement Abstract Now days applications should be require various types of images and pictures as sources of information for interpretation and analysis. Whenever an image is changed from one to another form such as, digitizing, scanning, transmitting and storing, some of the degradation always occurs at the output end. Hence, the output image has to go in a process called image enhancement which consists of a collection of techniques that need to improve the quality of an image. Image enhancement is basically improving image and its interpretation and perception of the information in digital images and providing good input for different other automated image processing techniques. The fuzzy set theory is always uncertainties (like it comes from the information available from situation such as darkness may result from incomplete, imprecise, and not fully reliable). The fuzzy logic gives a mathematical model for the representation and processing of good knowledge. The concept is depends upon i f-then rules in approximation of the variables likes threshold point. Also the Uncertainties within image processing tasks often due to vagueness and ambiguity. A fuzzy technique works as to manage these problems effectively. IndexTerms Fuzzy Logic, Image Processing, Image Enhancement, Image Fuzzification, Image Defuzzification Whenever an image is changed from one to another form such as, digitizing, scanning, transmitting and storing, some degradation is always occurs at the output stage. Hence, the output image has to go in a process called image enhancement. Image enhancement consists of a collection of techniques that need to improve the overall quality of an image. Fuzzy image processing is the approaches that understand, represent and process the images and their pixels with its values as fuzzy sets. The representation and processing is depending upon the selected fuzzy techniques and the problem to be solved. The idea of fuzzy sets is very simple and natural. For instance, if someone want to define a set of gray levels, one has to define a threshold for gray level from 0 to 100. Here 0 to 100 are element of this fuzzy set; the others do not belong to that set. The basis logic behind fuzzy technique is the basis for human communication. This observation depends upon many of the other statements about fuzzy logic. As fuzzy logic is built on the logics of qualitative description used in everyday language, fuzzy logic is very easy to use. A filtering system needs to be capable of reasoning with values and uncertain information; this suggests the use of fuzzy logic. II. FUZZY IMAGE PROCESSING OVERVIEW Fuzzy image processing techniques is not unique theory. It is a collection of different fuzzy approaches to image processing techniques. The following definition is to be regarded to determine the boundaries of fuzzy digital image processing: Fuzzy image processing is the approaches that understand, represent and process the digital images and their segments and also features as fuzzy sets. The representation of it and processing is always depending on the selected fuzzy techniques and on the problem which need to be solved [9]. Below a list of general observations is defined about fuzzy logic: Fuzzy logic is conceptually very easy to understand. The mathematical concepts behind fuzzy logic reasoning are simple. Fuzzy logic is important approach without the far-reaching complexity. Fuzzy logic is flexible. Everything is indefinite if you look closely enough, but more than that, most things are indefinite. Fuzzy reasoning prepared this understanding into the process rather than just theory. Fuzzy logic can model the nonlinear functions of mathematically complexity. One can create a fuzzy logic system to compare any sets of input and output data. This process is very easy by some of the adaptive techniques such as Adaptive Neuro-Fuzzy Inference Systems, which is already available in Fuzzy Logic Toolbox. Fuzzy logic can be design on the top of experience of experts. In case of neural networks, it must need training data and generate the outputs. But fuzzy logic will explain you about the experience of people who already understand the whole systems. Fuzzy logic can be mixed with any conventional control techniques. Fuzzy systems dont replace conventional control methods necessarily. Sometimes fuzzy systems increase them and simplify its implementation. Fuzzy logic is based on natural language communications. The basis for fuzzy logic is the basis for human communication and this observation explain many of the other statements about fuzzy logic as well. Actually Fuzzy logic is built on the structures of quality description used in everyday languages used for communications. Fuzzy logic is very easy to use. Natural language, which people used on a daily basis, has been comes by thousands of years of human history to be efficient. Sentences that are written in ordinary language always represent a triumph of efficient communication [3]. Fuzzy image processing has three stages: 1) Image Fuzzification 2) Modification of membership values 3) Image Defuzzification. Figure 1. Basic Fuzzy Image processing [5] The fuzzification and defuzzification steps are that in which we do not own fuzzy hardware. So, the coding of image data often called as fuzzification and decoding of the results called as defuzzification are the steps to process images with fuzzy techniques. The main thing of fuzzy image processing is in the intermediate stage that is modification of membership values (See Figure 1). After the image data are transformed from grey-level to the membership plane that is known as fuzzification is appropriate fuzzy techniques which modify the membership values which can be a fuzzy clustering and a fuzzy rule based approach and also it can be a fuzzy integration approach. The Fuzzy set theory Fuzzy set theory is the extension of crisp set theory. It works on the concept of partial truth (between 0 1). Completely true is 1 and completely false is 0. It was introduced by Prof. Lotfi A. Zadeh in 1965 as a mean to model the vagueness and ambiguity in complex systems [3]. Definition Fuzzy set A fuzzy set is a pair (A, m) where A is a set and m: A-> [0, 1]. For each, x A m(x) is called the grade of membership of x in (A, m). For a finite set A = {x1,,xn}, the fuzzy set (A, m) is denoted by {m(x1) / x1,,m(xn) / xn}. Let xà ¯Ã†â€™Ã… ½ A Then x is called not included in the fuzzy set (A, m) if m(x) = 0, x is called fully included if m(x) = 1, and x is called fuzzy member if 0 m(x) x à ¯Ã†â€™Ã… ½A |= m(x)>0} is called the support of (A, m) and the set {x A | m(x)=1} is called its kernel. Fuzzy sets is very easy and natural to understand. If one want to define a set of gray levels one have to determine a threshold, say the gray level from 0 to 100. All gray levels from 0 to 100 are element of this set; the others do not belong to the set (See Figure 2). But the darkness is a matter. A fuzzy set can be model this property in better way. For defining this set, it needs two different thresholds 50 and 150. All the gray levels which are less than 50 are the full member of this set and all the gray levels which are greater than 150 are not the member of this set at all. The gray levels that are between 50 and 150 have a partial membership in the set. Figure 2. Representation of dark gray-levels with a fuzzy and crisp set. Fuzzy Hyperbolization An image I of size MxNand L gray levels can be considered as anarray of fuzzy singletons and out of which each are having a value of membership denoted its brightness relative to its brightness levels Iwith I=0 to L-1. For an image I, we can write in the notation of fuzzy sets: Where g, is the intensity of (m, n)th pixel and  µmn its membership value. The membership function characterizes a suitable property of image (e.g. edginess, darkness, textural property) and it can be defined globally for the whole image or locally. The main principles of fuzzy image enhancement is illustrated in Figure. Figure 3. Fuzzy histogram hyperbolization image enhancements [2] Image Fuzzification The image fuzzification transforms the gray level of an image into values of membership function [0à ¢Ã¢â€š ¬Ã‚ ¦1]. 2 types of transformation functions are used, the triangle membership function, and Gaussian membership functions. A triangular membership functions is shown in Figure 4 and its equation is written as, Figure 4. Triangular membership functions The Gaussian membership function is shown in the Figure 5 and is characterized by two parameters {c, à Ã†â€™}. The equation for the Gaussian membership function is written as, Figure 5. Gaussian membership function Modification of Membership Function This process needs to change the values of the membership functions resulted from fuzzification process. In this algorithm, the shape of the membership function is set to triangular to characterize the hedges and value of the fuzzifier ÃŽÂ ². The fuzzifier ÃŽÂ ² is a linguistic hedge such that ÃŽÂ ² = -0.75 + ÃŽÂ ¼ 1.5, so that ÃŽÂ ² has a range of 0.5 2. The modification is carried out to the membership values by a hedges operator. The operation is called dilatation if the hedge operator ÃŽÂ ² is equal to 0.5 and it is called concentration if ÃŽÂ ² is equal to 2. If A is a fuzzy set and its represented as a set of ordered pairs of element x and its membership value is defined as ÃŽÂ ¼, then AÃŽÂ ² is the modified version of A and is indicated by below equation The hedge operator operates on the value of membership function as fuzzy linguistic hedges. Carrying hedge operator can be result in reducing image contrast or increasing image contrast, depending on the value of the ÃŽÂ ². The hedge operators may use to change the overall quality of the contrast of an image. Image Defuzzification After the values of fuzzy membership function has been modified, the next step is to generate the new gray level values. This process uses the fuzzy histogram hyperbolization. And this is due to the nonlinearity of human brightness perception. This algorithm modifies the membership values of gray levels by a logarithmic function: Where, ÃŽÂ ¼mn (gmn) is the gray level in the fuzzy membership values, ÃŽÂ ² is hedge operator, and gmn is the new gray level values. Fuzzy Inference System (FIS) Figure 6. Fuzzy Inference Systems Fuzzy inference is the process of mapping from the input-output using fuzzy logic. Mapping provides a basis from which it is possible to make the decisions. Process of fuzzy inference are mainly, the Membership Functions, the Logical Operations, and If-Then Rules. There are basically 2 types of fuzzy inference systems that is possible to implement in Fuzzy Logic Toolbox. 1) Mamdanitype and 2) Sugeno-type. These 2 types of inference systems vary in the way outputs are determined. Fuzzy inference systems has been successfully applied in fields such as data classification, decision analysis, automatic control and computer vision. As fuzzy is multidisciplinary, it can be used in fuzzy inference systems such as fuzzy-rule-based systems, fuzzy associative memory, fuzzy expert systems, fuzzy modeling, and fuzzy logic controllers, and simply fuzzy systems. Mamdanis fuzzy inference method is the most commonly used fuzzy method. Mamdanis method was the first control systems designed using fuzzy set theory. It was firstly proposed in 1975 by Ebrahim Mamdani [7] to control a steam engine and boiler combination by synthesizing a set of some linguistic control rules which can be obtained from experienced human operators. Mamdanis model was based on Lotfi Sades 1973 on fuzzy algorithms or complex systems and decision processes [8]. Mamdani-type inference, which defined for Fuzzy Logic Toolbox expects the output membership functions needs to be fuzzy sets. After the aggregation process, there is a fuzzy set for all the output variable that needs defuzzification. In many cases a single spike as an output membership functions are used. This type of output is usually known as a singleton output membership function. It always enhances the efficiency of the defuzzification process as it simplifies the computation required by the more simple Mamdani method, which finds the centroid of a 2D functions. Instead of integrating across the 2D function to find the centroid, one can use the weighted average of some of the data points. Sugeno-type system support this type of model. Sugeno-type systems can be used to design mathematical model of any inference system in which output membership functions are linear or constant. Fuzzy rule based system One other approach to infrared image contrast enhancement using fuzzy logic is a Takagi-Sugeno fuzzy rule based system. Takagi-Sugeno rules have consequents which are numeric functions of the input values. This approach is used to enhance the contrast of a gray-scale digital image which proposes the following rules: IF a pixel is dark, THEN make it darker IF a pixel is gray, THEN make it mid-gray IF a pixel is bright, THEN make it brighter Membership functions in a fuzzy set map all the elements of a set into some real numbers in the range [0, 1]. When the value of membership is higher, the truth that the set element belongs to that particular member function is higher as vice versa. The input membership functions for an image contrast enhancement system is shown in Figure 7. The set of all input image pixel values is mapped to 3 different linguistic terms: Dark, Gray Bright. The values ÃŽÂ ¼i(z) quantify the degree of membership of a particular input pixel intensity value to the each of the 3 member functions; denoted by the subscript (i). Thus, ÃŽÂ ¼dark(z) assigns value from 0 to 1 and in between to how truly dark an input pixel intensity value (z) is. Same way, ÃŽÂ ¼gray(z) and ÃŽÂ ¼bright(z) characterize how truly Gray or Bright a pixel value z is. The Dark and Bright input membership functions can be implemented by using a sigmoid functions and the Gray input membership function can be implemented by the Gaussian function. The sigmoid function, also known as the logistic function that is continuous and non-linear. This can be defined mathematically as follows: Where x is input and g(x) is gain. The Gaussian function is defined as below: Figure 7. Input Membership Functions for the Fuzzy Rule-Based Contrast Enhancement Three linguistic terms can be defined for the output member functions; and these are referred to as Darker, Mid-gray and Brighter. Because it is common in some of the implementations of Takagi-Sugeno systems, the output fuzzy sets are usually defined as fuzzy singleton that says the output membership functions are single-valued constants. Here the output membership function values can be selected as follows: Darker = 0 (ÃŽÂ ½d) Mid-gray = 127 (ÃŽÂ ½g) Brighter = 255 (ÃŽÂ ½b) These are shown below: Figure 8. Output Membership Functions for the Fuzzy Rule-Based Contrast Enhancement For a Takagi-Sugeno system design, the fuzzy logic rules which determine the outputs of system have been used the following linear combination of input and output membership function value. As the output membership functions are constants, the output ÃŽÂ ½o to any input zo, is given by: Where, ÃŽÂ ¼dark(z), ÃŽÂ ¼gray(z) and ÃŽÂ ¼bright(z) = the input pixel intensity values and (vd, vg and vb) = the output pixel intensity values. This relationship accomplishes the processes of implication, aggregation and defuzzification together with a numeric computation. In case of image processing, fuzzy logic is computationally intensive, as it requires the fuzzification, processing of all rules, implication, aggregation and the defuzzification on every pixel in the input digital image. Using a Takagi-Sugeno design which uses singleton output membership functions can reduce computational complexity Figure 9 is the block diagram of the process developed for the fuzzy logic technique implemented for this work. Figure 9. Flow chart for the implemented fuzzy logic process Contrast enhancement using an INT-Operator from fuzzy theory Many researchers have applied the fuzzy set theory to develop new techniques for contrast improvement. A basic fuzzy algorithm for image enhancement, using a global threshold, has been briefly recalled. Let us consider a gray level digital image, represented by the gray level values of the pixels with the range [0;1] and Let l be any gray level of a pixel in this digital image, l [0;1] . Contrast improvement is a basic point processing operation which mainly used to maximize the dynamic range of the image. A higher contrast in an image can be achieved by darkening the gray level in the lower luminance range and brightening the ones in the upper luminance range. This processing generally implies the use of a non-linear function; Form of such a function could be the one presented in Figure 10. Mathematical expression of such a nonlinear function, Int (l) is as below: The expression represents operator in the fuzzy set theory, namely the intensification (INT) operator. When it is applied on digital images, it has the effect of contrast enhancement. Figure 10. Fuzzy intensification Let us denote the resulting gray levels in the contrast enhanced image by g given by: Thus, the contrast enhanced image have gray levels of its pixels given by the nonlinear point-wise transformation in Figure 10, applied to the original gray level image. Implementation on Matlab The following are the steps which are carried out for the implementation to get the output: Read the original image. >> I = imread(Input image) Convert it into Gray Scale image if it is RGB image. >> I = rgb2gray(I) Add the noise to the image. >> Z = imnoise(I,gaussian,0.2); Calculate size of original image. >> [row col] = size(Z); Perform morphological operation on image. To find Maximum pixel value of image >> mx = max(max(Z)); To find Minimun pixel value of image >> mn = min(min(d)); To find Mid point of image >> mid = (mx+mn)/2; Apply fuzzy algorithm. Show the output. >> figure,imshow(output),title (output enhanced image) Conclusion Four different fuzzy approaches has been implemented to enhancement the high voltage images. Compared to the basic approaches, one can notice that fuzzy methods offer a powerful mathematical model for developing new enhancement algorithms. The global fuzzy approaches not gives satisfactory results. But here a locally adaptive procedure for fuzzy enhancement has been proposed. The contrast enhancement of high voltage images is also not satisfactory sometimes. The reason behind that is the physics of EPIDs which produces images with poor dynamics qualities and that is why sometimes there is no information in MVI to be improved. The fuzzy logic algorithms offer many different possibilities to optimize its performance, like parameters of membership functions, due to that it can certainly be expected that fuzzy image enhancement techniques can be applied in many areas of medical imaging in future. References [1]Farzam Farbiz, Mohammad Bager Menhaj, Seyed A. Motamedi, and Martin T. Hagan, A new Fuzzy Logic Filter for image Enhancement IEEE Transactions on Systems, Man, And Cybernetics-Part B: Cybernetics, Vol. 30, No. 1, February 2000. [2]Om Parkas Verma, Madasu Hanmandlu, Anil Singh Pariah and Vamp Krishna Madasu Fuzzy Filter for Noise Reduction in Color Images, ICGST-GVIP Journal, Vol. 9, No. 5, September 2009, pp.29-43. [3]Rafael C.Gonzalez and Richard, E. Woods Digital Image Processing, New Jersey, Pearson Prentice Hall, Third Edition 2008. [4]Aboul Ella Hassanien and Amr Bader, A comparative study on digital mammography Enhancement algorithms based on Fuzzy Theory, International Journal of Studies in Informatics and Control, SIC Volume 12 Number 1, March 2003, pp. 21-31. [5]Alper Pasha Morphological image processing with fuzzy logic, Aerospace and space technology magazines, Vol. 2, No. 3, 2006, pp.27-34. [6]Tamalika Chaira, Ajoy Kumar Ray, Fuzzy Image Processing and Applications with MATLAB, CRC Press, vol. 1, 2010,pp. 47-55. [7]Mamdani, E.H. and S. Assilian, An experiment in linguistic synthesis with a fuzzy logic controller, International Journal of Man-Machine Studies, Elsevier, Vol. 7, No. 1, 1975, pp. 1-13. [8]Zadeh, L.A., Outline of a new approach to the analysis of complex systems and decision processes, IEEE Transactions on Systems, Man, and Cybernetics, Vol. 3, No. 1, Jan. 1973, pp. 28-44. [9]H. R. Tizhoosh, G. Krell and B. Michaelis, On Fuzzy Enhancement of Megavoitage Images in Radiation Therapy, Proceedings of the 6th IEEE International Conference on Fuzzy Systems, July 1997. [10]Stefan Schulte, Valerie De Witte, and Etienn, E.Kerre, A Fuzzy Noise Reduction Method for Color Images, IEEE Transactions on Image Processing, Vol. 16, Issue 5, May 2007, pp. 1425-1436. [11]C.Castiello, G.Castellano, L.Caponetti and A.M.Fanelli, Fuzzy Classification of Image Pixels, IEEE International Symposium on Intelligent Signal Processing, 2003

Foreshadowing is the essential part of Steinbeck’s style in ‘Of Mice and Men’ Essay

Steinbeck, in my opinion, has one of the most unique styles of writing which is not only effective but also inspirational. The fact that he puts the whole plot and the ending right in front of us (at the beginning, in every section and even in the name) and we don’t recognise it easily is truly fascinating. Hints of the ‘grand finale’ could be found nearly everywhere in the novella. In the beginning of the play we learn that Lennie likes to pet soft things. He starts off by petting a mouse and then petting a puppy, of which he kills both as a result of his unrecognised brutal strength. The puppy was all innocent and fragile and Curley’s wife was seen in the same way which foreshadows the killing of Curley’s wife. The idea of Curley’s wife knowing the history of Lennie with pets and his blindness about the strength he possesses and still allowing him to stroke her hair was particularly considered peculiar by me. The only way I managed to justify this was that perhaps she was unaware of the dangers at that particular time as she was too caught up in the moment of perhaps she wanted to be rid of her depressive and oppressive life. Perhaps she was just fed up of her failure of her dreams and living a life of such misery that she thought of death to be the only way out and maybe death by the hands of Lennie seemed like a good idea because he was still considered to be childish meaning the element of innocence could be attached to him. Foreshadowing plays a huge role in indicating towards the fact that Lennie won’t make it alive to the end of the novella. The opening sets a pleasant mood to the story, it makes the world seem peaceful and lively then these feelings transforms into a darker and a much more sorrowful aura. The extract â€Å"I’ll put the old devil out of his misery right now† was said by Carlson to Candy. This action foreshadows the death of Lennie; He can be personified as Candy’s dog as his main purpose is also to accompany George hence when Lennie/ the dog is of no use or has exceeded his limit then he will be put down. This also highlights one of the theories attached to this story in perspective; the idea of Darwin’s ‘Survival of the fittest’ theory. The natural environment and settings could also emphasise Darwin’s theory as it was linked to nature but the quote also shows that a weak element in the food chain (Carlson) preys a weaker member of the food chain (the dog) and later on we learn that George (a weak member of the food chain) kills Lennie (a weaker member of the food chain in terms of mental abilities) regardless what the intents where. Furthermore, the death of Candy’s Dog and Lennie is pretty identical which can contribute to the method in which Lennie was murdered. The dog was shot in the head which was the same way Lennie was killed. The quotes â€Å"I ought to of shot that dog myself† and â€Å"He won’t even feel it.† shows that the idea of George killing Lennie himself rather than getting some else to do it is being suggested to George. Additionally, the second quotes highlights that it’s all for the best and this action is being taken for the greater good. In Addition, â€Å"I should have done it myself† said by Candy also suggests and foreshadows that George will be the murderer of Lennie. Candy realised afterwards and in a way regretted that he didn’t kill his dog, his best companion, himself, in the same George wouldn’t want Curley to kill Lennie so he would commit the murder himself. This could be considered a gesture of loyalty and love that now Lennie wouldn’t have to suffer anymore or it could also be a sign that George was just fed up of carrying Lennie’s burden around on his back. In my judgement, I would question George’s actions because I believe there are always other ways of getting around the situation and who gave George the right to take someone’s life. In conclusion, the element of foreshadowing is the main and one of the most important techniques which the novella is based on. Foreshadowing and hints of the future aspects of the story could be found at nearly every stage of the novella and I think that this doesn’t only make the story interesting but it also clamps the reader in deeper and deeper, it makes a reader’s hunger to find out the ending even more and more. In my opinion, Of Mice and Men is one of the most inspiring story’s I’ve read not only plot and content wise but also writer’s techniques wise.

Essay on The Mental Health of Individuals in the LGBT...

The mental health of individuals in the LGBT (lesbian, gay, bisexual, transgendered) community is something that is a serious problem. For most of the history of the United States and many different parts of the world LGBT people faced much persecution and in some cases even death. This constant fear of discovery and the pressure that one feels on oneself when â€Å"in the closet† can lead to major mental distress. Research has shown that people who identify as LGBT are twice as likely to develop lifetime mood and anxiety disorders (Bostwick 468). This is extremely noticeable the past couple years in the suicides of bullied teens on the basis of sexual identity and expression. The stigma on simply being perceived as LGBT is strong enough to†¦show more content†¦It is also worth noting that research into the mental health of the LGBT community is lacking (then and now) and that discrimination is legal in many cases (Luckstead 3). Some of the ways that discrimination is legal would be the banning of marriage rights in some states, the absence of sexuality and sexual expression in the working on discrimination clauses around the nation, the ability for a person to be fires or evicted simply because they are LGBT. All of these things can cause stress in the life of members of the LGBT community and in te cause of stress you find causes and additives to mental distress and illness. One could surmise that with the turning tide on the issue of gay rights in the US that the mental health risk here is not work looking into. This would be incorrect and the suicides of LGBT youth in the past couple years confirms this. The causes of these mental illnesses can be attributed heavily to discrimination on almost all fronts. The impact of institutional discrimination, that is discrimination that is passed through laws or amendments in a state or federal level, has been shown. 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