Alcoholic beverage Essay Example for Free

Alcoholic beverage Essay TGIF! How many people love to end a long, hard and stressful week with a cold beer in their hands? I know I do! Sixty-seven percent of U. S. adults drink alcohol, a slight increase over last year and the highest reading recorded since 1985 by one percentage point. Beer remains the favorite beverage among drinkers, followed by wine and then liquor. Usually people would think beer is only for drinking and getting a little buzz, but in reality, there is a deeper meaning to beer that is used for several of things that may surprise you. Beer is one of the worlds oldest prepared beverages; possibly dating back to the early Neolithic or 9500 BC, when cereal was first farmed, and is recorded in the written history of ancient Iraq and ancient Egypt. Archaeologists speculate that beer was instrumental in the formation of civilizations. Beer was the first alcoholic beverage known to civilization; however, who drank the first beer is unknown. Historians theorize that humankinds fondness for beer and other alcoholic beverages was a factor in our evolution away from a society of nomadic hunters and gathers into an agrarian society that would settle down to grow crops (and apparently drink). The first product humans made from grain water before learning to make bread was beer. Beer can help you with growing grass. How is beer and grass associated with each other? Next time you see brown spots on your lawns, pour beer on them. The grass will make use of the nutrients, sugar and energy in the beer and grow better. If you ever find slugs and snails in the grass, putting salt on them, to kill them, seems to be a big chore. Take a wide-mouthed container and half-fill it with beer. Bury it in your garden, in such a way that its mouth is at the ground level. Slugs and snails will be attracted to it and, in the process, get drowned. Aside from environmental uses, beer can also be beneficial to the objects around us. Want to bring the shine back to your gold ornaments? Put beer in a cup and dip a piece of cloth in it. Now, rub the gold object, without stones, with the dipped-in cloth. After it starts giving a renewed sign, dry it with a second cloth. Also, beer can polish wooden furniture. If you have some flattened beer left with you, use it to polish your old wooden furniture. Dampen a washcloth with beer and rub the cloth over the furniture, giving it more shiny and healthy look. Beer cannot only polish furniture but it also Removes Stains. For those looking for a solution to their stained clothes or carpet, beer will be the best bet. Pour beer onto the stain and gently rub the area with a brush. After the stain gets removed, blot the area with some water and let it dry. Beer also contains to personal uses. For instance, softening your hair. If you want to make your hair extra soft, make a shampoo with beer. Take half a can of beer and mix in a raw egg. Massage it into your hair and rinse well. You can also rub in a mixture of 15ml beer and 70ml warm water into your hair and rinse it off. This substance can also remove foot odor. You can use beer to remove bad odor from your feet. Wash your feet with beer and then rinse them with soap and water. Dry your feet with a towel and put some talcum powder on them. In addition, it can also remove stains, For those looking for a solution to their stained clothes or carpet, beer will be the best bet. Pour beer onto the stain and gently rub the area with a brush. After the stain gets removed, blot the area with some water and let it dry. Since beer was discovered, it has been in many different cultures and regions. The usage of beer plays a different role in various of countries. Beer Traditions The Honeymoon 4,000 years ago in Babylon, it was an accepted practice that for a month after the wedding, the brides father would supply his son-in-law with all the mead or beer he could drink. In ancient Babylon, the calendar was lunar-based based on the cycle of the moon. The month following any wedding was called the honey month which evolved into honeymoon. Mead is a honey beer and what better way to celebrate a honeymoon. Pubs in Ireland, and you won’t find TVs lining the walls, but people sitting around in circles, drinking pints, and talking with one another. The activity of drinking beer has taken on creative forms to include drinking games, such as beer pong, or visiting multiple bars and pubs in an evening to enjoy a beer from each one. As the consumption of beer has continued to grow in popularity, with beer being one of the most consumed beverages in the world, beer festivals have increased. Most notable is the German festival, Oktoberfest, which just ended. Over 200 years old, the festival began in Germany and has grown to feature cities across the world holding their own Oktoberfest events, with live entertainment, German food, and beer. As the number of breweries has increased, they have begun to partner with nearby cities to hold their own beer festivals. You can often find a number of regional and national beer festivals taking place almost any month of the year. Beer and nutrition? You dont usually see those two words together, but perhaps beer is a bit misunderstood. It may actually be good for you when consumed in moderate amounts. Beer has been brewed for just about as long as humans have been cultivating crops and is actually made with some very healthy ingredients. Those ingredients are hops, brewers yeast, barley and malt. There are different styles of beer and each style has a distinctive flavor and color. Tasting and learning about the different types of beer is as much fun as tasting and learning about the different types of wine. Drinking one beer per day may be good for your health because it has been associated with a lower risk of cardiovascular disease. Why? Experts suggested that the folate found in beer may help to reduce homocysteine in the blood and lower homocysteine levels mean a lower risk of cardiovascular disease. Lab studies have found constituents in beer that lower triglycerides and LDL cholesterol in mice. Drinking one beer per day reduces blood clotting so some studies found that cardiovascular patients who drank one beer per day also lived longer. Other studies have found that women who consume one beer each day have improved mental health. Drinking beer and other alcoholic beverages in moderation may also improve bone density. Of course that doesnt mean that if one beer is good, three or four must be better. That isnt true. Drinking more than one beer or any alcoholic beverage per day can put too much alcohol in your system and that isnt good for you. Heavy drinking has been associated with several health problems, so moderation is definitely the key with drinking beer. The studies also point to one beer per day as being beneficial, not drinking all seven beers in one day per week. That type of binge drinking will overload you system with alcohol too. The benefits of beer nutrition probably have nothing to do with the alcohol and there are some low-alcohol beers and non-alcohol beers available which offer the same heart-protective effect as regular and light beers.

Performance Measure of PCA and DCT for Images

Performance Measure of PCA and DCT for Images Generally, in Image Processing the transformation is the basic technique that we apply in order to study the characteristics of the Image under scan. Under this process here we present a method in which we are analyzing the performance of the two methods namely, PCA and DCT. In this thesis we are going to analyze the system by first training the set for particular no. Of images and then analyzing the performance for the two methods by calculating the error in this two methods. This thesis referred and tested the PCA and DCT transformation techniques. PCA is a technique which involves a procedure which mathematically transforms number of probably related parameters into smaller number of parameters whose values dont change called principal components. The primary principal component accounts for much variability in the data, and each succeeding component accounts for much of the remaining variability. Depending on the application field, it is also called the separate Karhunen-Loà ¨ve transform (KLT), the Hotelling transform or proper orthogonal decomposition (POD). DCT expresses a series of finitely many data points in terms of a sum of cosine functions oscillating at different frequencies. Transformations are important to numerous applications in science and engineering, from lossy compression of audio and images (where small high-frequency components can be discarded), to spectral methods for the numerical solution of partial differential equations. CHAPTER 1 INTRODUCTION 1.1 Introduction Over the past few years, several face recognition systems have been proposed based on principal components analysis (PCA) [14, 8, 13, 15, 1, 10, 16, 6]. Although the details vary, these systems can all be described in terms of the same preprocessing and run-time steps. During preprocessing, they register a gallery of m training images to each other and unroll each image into a vector of n pixel values. Next, the mean image for the gallery is subtracted from each  and the resulting centered images are placed in a gallery matrix M. Element [i; j] of M is the ith pixel from the jth image. A covariance matrix W = MMT characterizes the distribution of the m images in Ân. A subset of the Eigenvectors of W are used as the basis vectors for a subspace in which to compare gallery and novel probe images. When sorted by decreasing Eigenvalue, the full set of unit length Eigenvectors represent an orthonormal basis where the first direction corresponds to the direction of maximum variance i n the images, the second the next largest variance, etc. These basis vectors are the Principle Components of the gallery images. Once the Eigenspace is computed, the centered gallery images are projected into this subspace. At run-time, recognition is accomplished by projecting a centered  probe image into the subspace and the nearest gallery image to the probe image is selected as its match. There are many differences in the systems referenced. Some systems assume that the images are registered prior to face recognition [15, 10, 11, 16]; among the rest, a variety of techniques are used to identify facial features and register them to each other. Different systems may use different distance measures when matching probe images to the nearest gallery image. Different systems select different numbers of Eigenvectors (usually those corresponding to the largest k Eigenvalues) in order to compress the data and to improve accuracy by eliminating Eigenvectors corresponding to noise rather than meaningful variation. To help evaluate and compare individual steps of the face recognition process, Moon and Phillips created the FERET face database, and performed initial comparisons of some common distance measures for otherwise identical systems [10, 11, 9]. This paper extends their work, presenting further comparisons of distance measures over the FERET database and examining alternative way of selecting subsets of Eigenvectors. The Principal Component Analysis (PCA) is one of the most successful techniques that have been used in image recognition and compression. PCA is a statistical method under the broad title of factor analysis. The purpose of PCA is to reduce the large dimensionality of the data space (observed variables) to the smaller intrinsic dimensionality of feature space (independent variables), which are needed to describe the data economically. This is the case when there is a strong correlation between observed variables. The jobs which PCA can do are pred iction, redundancy removal, feature extraction, data compression, etc. Because PCA is a classical technique which can do something in the linear domain, applications having linear models are suitable, such as signal processing, image processing, system and control theory, communications, etc. Face recognition has many applicable areas. Moreover, it can be categorized into face identification, face classification, or sex determination. The most useful applications contain crowd surveillance, video content indexing, personal identification (ex. drivers license), mug shots matching, entrance security, etc. The main idea of using PCA for face recognition is to express the large 1-D vector of pixels constructed from 2-D facial image into the compact principal components of the feature space. This can be called eigen space projection. Eigen space is calculated by identifying the eigenvectors of the covariance matrix derived from a set of facial images(vectors). The details are described i n the following section. PCA computes the basis of a space which is represented by its training vectors. These basis vectors, actually eigenvectors, computed by PCA are in the direction of the largest variance of the training vectors. As it has been said earlier, we call them eigenfaces. Each eigenface can be viewed a feature. When a particular face is projected onto the face space, its vector into the face space describe the importance of each of those features in the face. The face is expressed in the face space by its eigenface coefficients (or weights). We can handle a large input vector, facial image, only by taking its small weight vector in the face space. This means that we can reconstruct the original face with some error, since the dimensionality of the image space is much larger than that of face space. A face recognition system using the Principal Component Analysis (PCA) algorithm. Automatic face recognition systems try to find the identity of a given face image according to their memory. The memory of a face recognizer is generally simulated by a training set. In this project, our training set consists of the features extracted from known face images of different persons. Thus, the task of the face recognizer is to find the most similar feature vector among the training set to the feature vector of a given test image. Here, we want to recognize the identity of a person where an image of that person (test image) is given to the system. You will use PCA as a feature extraction algorithm in this project. In the training phase, you should extract feature vectors for each image in the training set. Let  ­A be a training image of person A which has a pixel resolution of M  £ N (M rows, N columns). In order to extract PCA features of  ­A, you will first convert the image into a pixel vector à A by concatenating each of the M rows into a single vector. The length (or, dimensionality) of the vector à A will be M  £N. In this project, you will use the PCA algorithm as a dimensionality reduction technique which transforms the vector à A to a vector !A which has a imensionality d where d  ¿ M  £ N. For each training image  ­i, you should calculate and store these feature vectors !i. In the recognition phase (or, testing phase), you will be given a test image  ­j of a known person. Let  ®j be the identity (name) of this person. As in the training phase, you should compute the feature vector of this person using PCA and obtain !j . In order to identify  ­j , you should compute the similarities between !j and all of the feature vectors !is in the training set. The similarity between feature vectors can be computed using Euclidean distance. The identity of the most similar !i will be the output of our face recogn izer. If i = j, it means that we have correctly identified the person j, otherwise if i 6= j, it means that we have misclassified the person j. 1.2 Thesis structure: This thesis work is divided into five chapters as follows. Chapter 1: Introduction This introductory chapter is briefly explains the procedure of transformation in the Face Recognition and its applications. And here we explained the scope of this research. And finally it gives the structure of the thesis for friendly usage. Chapter 2: Basis of Transformation Techniques. This chapter gives an introduction to the Transformation techniques. In this chapter we have introduced two transformation techniques for which we are going to perform the analysis and result are used for face recognition purpose Chapter 3: Discrete Cosine Transformation In this chapter we have continued the part from chapter 2 about transformations. In this other method ie., DCT is introduced and analysis is done Chapter 4: Implementation and results This chapter presents the simulated results of the face recognition analysis using MATLAB. And it gives the explanation for each and every step of the design of face recognition analysis and it gives the tested results of the transformation algorithms. Chapter 5: Conclusion and Future work This is the final chapter in this thesis. Here, we conclude our research and discussed about the achieved results of this research work and suggested future work for this research. CHAPTER 2 BASICs of Image Transform Techniques 2.1 Introduction: Now a days Image Processing has been gained so much of importance that in every field of science we apply image processing for the purpose of security as well as increasing demand for it. Here we apply two different transformation techniques in order study the performance which will be helpful in the detection purpose. The computation of the performance of the image given for testing is performed in two steps: PCA (Principal Component Analysis) DCT (Discrete Cosine Transform) 2.2 Principal Component Analysis: PCA is a technique which involves a procedure which mathematically transforms number of possibly correlated variables into smaller number of uncorrelated variables called principal components. The first principal component accounts for much variability in the data, and each succeeding component accounts for much of the remaining variability. Depending on the application field, it is also called the discrete Karhunen-Loà ¨ve transform (KLT), the Hotelling transform or proper orthogonal decomposition (POD). Now PCA is mostly used as a tool in exploration of data analysis and for making prognostic models. PCA also involves calculation for the Eigen value decomposition of a data covariance matrix or singular value decomposition of a data matrix, usually after mean centring the data from each attribute. The results of this analysis technique are usually shown in terms of component scores and also as loadings. PCA is real Eigen based multivariate analysis. Its action can be termed in terms of as edifying the inner arrangement of the data in a shape which give details of the mean and variance in the data. If there is any multivariate data then its visualized as a set if coordinates in a multi dimensional data space, this algorithm allows the users having pictures with a lower aspect reveal a shadow of object in view from a higher aspect view which reveals the true informative nature of the object. PCA is very closely related to aspect analysis, some statistical software packages purposely conflict the two techniques. True aspect analysis makes different assumptions about the original configuration and then solves eigenvectors of a little different medium. 2.2.1 PCA Implementation: PCA is mathematically defined as an orthogonal linear transformation technique that transforms data to a new coordinate system, such that the greatest variance from any projection of data comes to lie on the first coordinate, the second greatest variance on the second coordinate, and so on. PCA is theoretically the optimum transform technique for given data in least square terms. For a data matrix, XT, with zero empirical mean ie., the empirical mean of the distribution has been subtracted from the data set, where each row represents a different repetition of the experiment, and each column gives the results from a particular probe, the PCA transformation is given by: Where the matrix ÃŽÂ £ is an m-by-n diagonal matrix, where diagonal elements ae non-negative and W  ÃƒÅ½Ã‚ £Ãƒâ€šÃ‚  VT is the singular value decomposition of  X. Given a set of points in Euclidean space, the first principal component part corresponds to the line that passes through the mean and minimizes the sum of squared errors with those points. The second principal component corresponds to the same part after all the correlation terms with the first principal component has been subtracted from the points. Each Eigen value indicates the part of the variance ie., correlated with each eigenvector. Thus, the sum of all the Eigen values is equal to the sum of squared distance of the points with their mean divided by the number of dimensions. PCA rotates the set of points around its mean in order to align it with the first few principal components. This moves as much of the variance as possible into the first few dimensions. The values in the remaining dimensions tend to be very highly correlated and may be dropped with minimal loss of information. PCA is used for dimensionality reduction. PCA is optimal linear transformation technique for keep ing the subspace which has largest variance. This advantage comes with the price of greater computational requirement. In discrete cosine transform, Non-linear dimensionality reduction techniques tend to be more computationally demanding in comparison with PCA. Mean subtraction is necessary in performing PCA to ensure that the first principal component describes the direction of maximum variance. If mean subtraction is not performed, the first principal component will instead correspond to the mean of the data. A mean of zero is needed for finding a basis that minimizes the mean square error of the approximation of the data. Assuming zero empirical mean (the empirical mean of the distribution has been subtracted from the data set), the principal component w1 of a data set x can be defined as: With the first k  Ãƒ ¢Ã‹â€ Ã¢â‚¬â„¢Ãƒâ€šÃ‚  1 component, the kth component can be found by subtracting the first k à ¢Ã‹â€ Ã¢â‚¬â„¢ 1 principal components from x: and by substituting this as the new data set to find a principal component in The other transform is therefore equivalent to finding the singular value decomposition of the data matrix X, and then obtaining the space data matrix Y by projecting X down into the reduced space defined by only the first L singular vectors, WL: The matrix W of singular vectors of X is equivalently the matrix W of eigenvectors of the matrix of observed covariances C = X XT, The eigenvectors with the highest eigen values correspond to the dimensions that have the strongest correlation in the data set (see Rayleigh quotient). PCA is equivalent to empirical orthogonal functions (EOF), a name which is used in meteorology. An auto-encoder neural network with a linear hidden layer is similar to PCA. Upon convergence, the weight vectors of the K neurons in the hidden layer will form a basis for the space spanned by the first K principal components. Unlike PCA, this technique will not necessarily produce orthogonal vectors. PCA is a popular primary technique in pattern recognition. But its not optimized for class separability. An alternative is the linear discriminant analysis, which does take this into account. 2.2.2 PCA Properties and Limitations PCA is theoretically the optimal linear scheme, in terms of least mean square error, for compressing a set of high dimensional vectors into a set of lower dimensional vectors and then reconstructing the original set. It is a non-parametric analysis and the answer is unique and independent of any hypothesis about data probability distribution. However, the latter two properties are regarded as weakness as well as strength, in that being non-parametric, no prior knowledge can be incorporated and that PCA compressions often incur loss of information. The applicability of PCA is limited by the assumptions[5] made in its derivation. These assumptions are: We assumed the observed data set to be linear combinations of certain basis. Non-linear methods such as kernel PCA have been developed without assuming linearity. PCA uses the eigenvectors of the covariance matrix and it only finds the independent axes of the data under the Gaussian assumption. For non-Gaussian or multi-modal Gaussian data, PCA simply de-correlates the axes. When PCA is used for clustering, its main limitation is that it does not account for class separability since it makes no use of the class label of the feature vector. There is no guarantee that the directions of maximum variance will contain good features for discrimination. PCA simply performs a coordinate rotation that aligns the transformed axes with the directions of maximum variance. It is only when we believe that the observed data has a high signal-to-noise ratio that the principal components with larger variance correspond to interesting dynamics and lower ones correspond to noise. 2.2.3 Computing PCA with covariance method Following is a detailed description of PCA using the covariance method . The goal is to transform a given data set X of dimension M to an alternative data set Y of smaller dimension L. Equivalently; we are seeking to find the matrix Y, where Y is the KLT of matrix X: Organize the data set Suppose you have data comprising a set of observations of M variables, and you want to reduce the data so that each observation can be described with only L variables, L Write as column vectors, each of which has M rows. Place the column vectors into a single matrix X of dimensions M ÃÆ'- N. Calculate the empirical mean Find the empirical mean along each dimension m = 1,  ,  M. Place the calculated mean values into an empirical mean vector u of dimensions M ÃÆ'- 1. Calculate the deviations from the mean Mean subtraction is an integral part of the solution towards finding a principal component basis that minimizes the mean square error of approximating the data. Hence we proceed by centering the data as follows: Subtract the empirical mean vector u from each column of the data matrix X. Store mean-subtracted data in the M ÃÆ'- N matrix B. where h is a 1  ÃƒÆ'-  N row vector of all  1s: Find the covariance matrix Find the M ÃÆ'- M empirical covariance matrix C from the outer product of matrix B with itself: where is the expected value operator, is the outer product operator, and is the conjugate transpose operator. Please note that the information in this section is indeed a bit fuzzy. Outer products apply to vectors, for tensor cases we should apply tensor products, but the covariance matrix in PCA, is a sum of outer products between its sample vectors, indeed it could be represented as B.B*. See the covariance matrix sections on the discussion page for more information. Find the eigenvectors and eigenvalues of the covariance matrix Compute the matrix V of eigenvectors which diagonalizes the covariance matrix C: where D is the diagonal matrix of eigenvalues of C. This step will typically involve the use of a computer-based algorithm for computing eigenvectors and eigenvalues. These algorithms are readily available as sub-components of most matrix algebra systems, such as MATLAB[7][8], Mathematica[9], SciPy, IDL(Interactive Data Language), or GNU Octave as well as OpenCV. Matrix D will take the form of an M ÃÆ'- M diagonal matrix, where is the mth eigenvalue of the covariance matrix C, and Matrix V, also of dimension M ÃÆ'- M, contains M column vectors, each of length M, which represent the M eigenvectors of the covariance matrix C. The eigenvalues and eigenvectors are ordered and paired. The mth eigenvalue corresponds to the mth eigenvector. Rearrange the eigenvectors and eigenvalues Sort the columns of the eigenvector matrix V and eigenvalue matrix D in order of decreasing eigenvalue. Make sure to maintain the correct pairings between the columns in each matrix. Compute the cumulative energy content for each eigenvector The eigenvalues represent the distribution of the source datas energy among each of the eigenvectors, where the eigenvectors form a basis for the data. The cumulative energy content g for the mth eigenvector is the sum of the energy content across all of the eigenvalues from 1 through m: Select a subset of the eigenvectors as basis vectors Save the first L columns of V as the M ÃÆ'- L matrix W: where Use the vector g as a guide in choosing an appropriate value for L. The goal is to choose a value of L as small as possible while achieving a reasonably high value of g on a percentage basis. For example, you may want to choose L so that the cumulative energy g is above a certain threshold, like 90 percent. In this case, choose the smallest value of L such that Convert the source data to z-scores Create an M ÃÆ'- 1 empirical standard deviation vector s from the square root of each element along the main diagonal of the covariance matrix C: Calculate the M ÃÆ'- N z-score matrix: (divide element-by-element) Note: While this step is useful for various applications as it normalizes the data set with respect to its variance, it is not integral part of PCA/KLT! Project the z-scores of the data onto the new basis The projected vectors are the columns of the matrix W* is the conjugate transpose of the eigenvector matrix. The columns of matrix Y represent the Karhunen-Loeve transforms (KLT) of the data vectors in the columns of matrix  X. 2.2.4 PCA Derivation Let X be a d-dimensional random vector expressed as column vector. Without loss of generality, assume X has zero mean. We want to find a Orthonormal transformation matrix P such that with the constraint that is a diagonal matrix and By substitution, and matrix algebra, we obtain: We now have: Rewrite P as d column vectors, so and as: Substituting into equation above, we obtain: Notice that in , Pi is an eigenvector of the covariance matrix of X. Therefore, by finding the eigenvectors of the covariance matrix of X, we find a projection matrix P that satisfies the original constraints. CHAPTER 3 DISCRETE Cosine transform 3.1 Introduction: A discrete cosine transform (DCT) expresses a sequence of finitely many data points in terms of a sum of cosine functions oscillating at different frequencies. DCTs are important to numerous applications in engineering, from lossy compression of audio and images, to spectral methods for the numerical solution of partial differential equations. The use of cosine rather than sine functions is critical in these applications: for compression, it turns out that cosine functions are much more efficient, whereas for differential equations the cosines express a particular choice of boundary conditions. In particular, a DCT is a Fourier-related transform similar to the discrete Fourier transform (DFT), but using only real numbers. DCTs are equivalent to DFTs of roughly twice the length, operating on real data with even symmetry (since the Fourier transform of a real and even function is real and even), where in some variants the input and/or output data are shifted by half a sample. There are eight standard DCT variants, of which four are common. The most common variant of discrete cosine transform is the type-II DCT, which is often called simply the DCT; its inverse, the type-III DCT, is correspondingly often called simply the inverse DCT or the IDCT. Two related transforms are the discrete sine transforms (DST), which is equivalent to a DFT of real and odd functions, and the modified discrete cosine transforms (MDCT), which is based on a DCT of overlapping data. 3.2 DCT forms: Formally, the discrete cosine transform is a linear, invertible function F  : RN -> RN, or equivalently an invertible N ÃÆ'- N square matrix. There are several variants of the DCT with slightly modified definitions. The N real numbers x0, , xN-1 are transformed into the N real numbers X0, , XN-1 according to one of the formulas: DCT-I Some authors further multiply the x0 and xN-1 terms by à ¢Ã‹â€ Ã… ¡2, and correspondingly multiply the X0 and XN-1 terms by 1/à ¢Ã‹â€ Ã… ¡2. This makes the DCT-I matrix orthogonal, if one further multiplies by an overall scale factor of , but breaks the direct correspondence with a real-even DFT. The DCT-I is exactly equivalent, to a DFT of 2N à ¢Ã‹â€ Ã¢â‚¬â„¢ 2 real numbers with even symmetry. For example, a DCT-I of N=5 real numbers abcde is exactly equivalent to a DFT of eight real numbers abcdedcb, divided by two. Note, however, that the DCT-I is not defined for N less than 2. Thus, the DCT-I corresponds to the boundary conditions: xn is even around n=0 and even around n=N-1; similarly for Xk. DCT-II The DCT-II is probably the most commonly used form, and is often simply referred to as the DCT. This transform is exactly equivalent to a DFT of 4N real inputs of even symmetry where the even-indexed elements are zero. That is, it is half of the DFT of the 4N inputs yn, where y2n = 0, y2n + 1 = xn for , and y4N à ¢Ã‹â€ Ã¢â‚¬â„¢ n = yn for 0 Some authors further multiply the X0 term by 1/à ¢Ã‹â€ Ã… ¡2 and multiply the resulting matrix by an overall scale factor of . This makes the DCT-II matrix orthogonal, but breaks the direct correspondence with a real-even DFT of half-shifted input. The DCT-II implies the boundary conditions: xn is even around n=-1/2 and even around n=N-1/2; Xk is even around k=0 and odd around k=N. DCT-III Because it is the inverse of DCT-II (up to a scale factor, see below), this form is sometimes simply referred to as the inverse DCT (IDCT). Some authors further multiply the x0 term by à ¢Ã‹â€ Ã… ¡2 and multiply the resulting matrix by an overall scale factor of , so that the DCT-II and DCT-III are transposes of one another. This makes the DCT-III matrix orthogonal, but breaks the direct correspondence with a real-even DFT of half-shifted output. The DCT-III implies the boundary conditions: xn is even around n=0 and odd around n=N; Xk is even around k=-1/2 and even around k=N-1/2. DCT-IV The DCT-IV matrix becomes orthogonal if one further multiplies by an overall scale factor of . A variant of the DCT-IV, where data from different transforms are overlapped, is called the modified discrete cosine transform (MDCT) (Malvar, 1992). The DCT-IV implies the boundary conditions: xn is even around n=-1/2 and odd around n=N-1/2; similarly for Xk. DCT V-VIII DCT types I-IV are equivalent to real-even DFTs of even order, since the corresponding DFT is of length 2(Nà ¢Ã‹â€ Ã¢â‚¬â„¢1) (for DCT-I) or 4N (for DCT-II/III) or 8N (for DCT-VIII). In principle, there are actually four additional types of discrete cosine transform, corresponding essentially to real-even DFTs of logically odd order, which have factors of N ±Ãƒâ€šÃ‚ ½ in the denominators of the cosine arguments. Equivalently, DCTs of types I-IV imply boundaries that are even/odd around either a data point for both boundaries or halfway between two data points for both boundaries. DCTs of types V-VIII imply boundaries that even/odd around a data point for one boundary and halfway between two data points for the other boundary. However, these variants seem to be rarely used in practice. One reason, perhaps, is that FFT algorithms for odd-length DFTs are generally more complicated than FFT algorithms for even-length DFTs (e.g. the simplest radix-2 algorithms are only for even lengths), and this increased intricacy carries over to the DCTs as described below. Inverse transforms Using the normalization conventions above, the inverse of DCT-I is DCT-I multiplied by 2/(N-1). The inverse of DCT-IV is DCT-IV multiplied by 2/N. The inverse of DCT-II is DCT-III multiplied by 2/N and vice versa. Like for the DFT, the normalization factor in front of these transform definitions is merely a convention and differs between treatments. For example, some authors multiply the transforms by so that the inverse does not require any additional multiplicative factor. Combined with appropriate factors of à ¢Ã‹â€ Ã… ¡2 (see above), this can be used to make the transform matrix orthogonal. Multidimensional DCTs Multidimensional variants of the various DCT types follow straightforwardly from the one-dimensional definitions: they are simply a separable product (equivalently, a composition) of DCTs along each dimension. For example, a two-dimensional DCT-II of an image or a matrix is simply the one-dimensional DCT-II, from above, performed along the rows and then along the columns (or vice versa). That is, the 2d DCT-II is given by the formula (omitting normalization and other scale factors, as above): Two-dimensional DCT frequencies Technically, computing a two- (or multi-) dimensional DCT by sequences of one-dimensional DCTs along each dimension is known as a row-column algorithm. As with multidimensional FFT algorithms, however, there exist other methods to compute the same thing while performing the computations in a different order. The inverse of a multi-dimensional DCT is just a separable product of the inverse(s) of the corresponding one-dimensional DCT(s), e.g. the one-dimensional inverses applied along one dimension at a time in a row-column algorithm. The image to the right shows combination of horizontal and vertical frequencies for an 8 x 8 (N1 = N2 = 8) two-dimensional DCT. Each step from left to right and top to bottom is an increase in frequency by 1/2 cycle. For example, moving right one from the top-left square yields a half-cycle increase in the horizontal frequency. Another move to the right yields two half-cycles. A move down yields two half-cycles horizontally and a half-cycle vertically. The source data (88) is transformed to a linear combination of these 64 frequency squares. Chapter 4 IMPLEMENTATION AND RESULTS 4.1 Introduction: In previous chapters (chapter 2 and chapter 3), we get the theoretical knowledge about the Principal Component Analysis and Discrete Cosine Transform. In our thesis work we have seen the analysis of both transform. To execute these tasks we chosen a platform called MATLAB, stands for matrix laboratory. It is an efficient language for Digital image processing. The image processing toolbox in MATLAB is a collection of different MATAB functions that extend the capability of the MATLAB environment for the solution of digital image processing problems. [13] 4.2 Practical implementation of Performance analysis: As discussed earlier we are going to perform analysis for the two transform methods, to the images as, <

Walmart Business Analysis

Walmart Business Analysis Contents (Jump to) Walmart’s Current Strategy Organizational structure, culture, and control systems SWOT Analysis for Walmart Porter’s Five Analysis of Walmart Key Strategic Issues at Walmart Personal SWOT Analysis Financial Analysis of Walmart Recommendations Walmart store Inc. is not only the retail giant, but also is the largest grocery chain in the world. Walmart store Inc. was founded in 1962. Samuel Walton and his brother J.L. Walton open their first Walmart Discount City in Rogers, Arkansas (Walmart History, 2010). For Walmart store Inc., their common mission is: Save people money so they can live better (Walmart corporate, 2010). Compared with their main competitors such as Target and K mart, Walmarts 2009 sales were almost 50% more. Because of its giant size and buying power, Walmart can buy its products at very low prices, exchanging high purchase volumes for low cost then passing the savings onto its customers (Wikinvest Walmart, 2010). Walmart has 8,900 stores around the world in three different business segments of retail stores that including: Walmart stores, Sams Club and Walmart international. All of them offer different kinds of merchandises including electronic appliances, groceries, furniture, apparel and health beauty stuffs etc. For their business segment, they have over 54% of the companys stores are located in the United States, and the others international stores are mainly located in central and south America and China. The company mainly focuses on offering the lowest prices to attract its consumers. Walmart totally earned $408 billion revenue in 2010, increase 1% compare to 2009 (Wikinvest Walmart, 2010). In 2009, Walmart earned $255.7 billion in the domestic segment of the companys revenue. For Walmart stores segment are further categories into three different formats including: Supercenters, Discount stores and Neighborhood Stores. For the Sams club, it is the second largest membership-only retailer club ( Costco is the first largest membership-only retailer) in United States belong to Walmart Inc., their main customers mostly are offices, convenience stores, motels, restaurants and schools etc. (Wikinvest Walmart, 2010). For now, Walmart has total 3,121 international stores all over the world including in Mexico, Japan, Canada, China and countries in central and South America. However, recently Walmart begins to slow down their growth rate in the United State and turn their main focus onto its international stores to develop growth. For international stores locations altogether earned total $98.6 billion revenue in 2009, compared to the sales of 2008, is increased 9.1% (Wikinvest Walmart, 2010). Strategic History of the Industry The whole retail industry in the United States has over $4 trillion annual revenue. The main retail companies are including Walmart, Home Depot, Kroger, Costco, and Target. Some of the large companies dominate some retail sectors such as mass merchandisers and grocery stores, other sectors like auto dealers and convenience stores are fragmented. However, retail industry still has many small and specialty retailers are single-store operations (Hoover, 2011). The economy deeply affects the retail demand. In other words, retail demand depends on the economy. Many different kinds of economic factors such as job growth, recession, personal income, consumer confidence and interest rates can strongly affect consumer spending behavior. When during recessionary periods, the bad economy can affect the retail sales growth rate slow drastically or even sales revenue decline. While the retail spending grows rapidly when in the period of strong economy growth, for example consumers will spend more on grocery when they have more income. However, the rising interest rates will affect consumer purchase behavior and consumer ability to finance large amount of purchase such as purchasing cars (Hoover, 2011). Strategic History of Walmart Store Inc. In the early stage of strategic history for Walmart, they always unchanged their vision always low price for their customers. Until 1990s, Walmart announced that they planned to go global. They wanted to look for international markets for the reasons as following: First of all, Walmart has facing very strong competition in United States such as Target and K mart. These two firms had aggressive expanding their business and had started sharing Walmarts market share. Secondly, the market in the United States is already saturated; it was becoming difficult for the company to continue its growth rate. Thirdly, the US population is accounted for only 4% of the worlds population and if they want to expand their global market, China had the potential massive growth due to their huge population of over 1.3 billion people. The last reason is, globalization opened up new markets in China and created opportunities for discount stores such as Walmart (Walmarts Cost Leadership Strategy, 2004). On the other hand, Walmart is using the strategy that cooperates with local suppliers to purchase their products, even though the organizational culture is standardized with the home country. This strategy is not only use to the products purchasing, but also adapted to the local cultures and stores decoration and designed are also changed to meet local taste all around the world (Walmarts Cost Leadership Strategy, 2004). Organizational mission statement As we know, the mission statement for Walmart is every day low price. In order to insist their mission, Walmart implemented three approaches in the market. First, it increased the local purchasing in order to reduce the purchasing costs and also suit consumers needs in different places. Secondly, it maintained a good relationship with their suppliers, satisfied them by paying within 3-7 days during its initial years. Thirdly, it established distribution centers (DC) and computerized its management system to improve efficiency and reduce costs (ICFAI, 2005). Business Level Strategy For these several years, Walmart has been trying hard on expand its stores outside the United States. It through two different to expand their international business market: new store construction and acquisition. Acquisition strategy of supermarket chains had been a part of Walmarts entry and  store expansion strategy in Canada, Mexico, Brazil, Japan, China and Great Britain (The Walmart Puzzle, 2008). Over all, the Walmart strategies were including: multiple store segments, lower daily prices, lots of name-brand merchandise, reduce operating costs, emphasized customers satisfied service, wide selection products, disciplined expansion into new geographic markets, and using acquisition to enter foreign market (Walmart Store Inc., 2010). However, no matter Walmart are in which foreign country, their company vision always low prices is never changed. The companys low distribution costs and cost-efficient supply chain management are the big reasons why Walmart is so success and at the same time reduce the products prices. Walmart has get into distribution efficiency compare with their competitors because of its rural store locations. Current strategy for the major operations/functions of the company Current strategies for Walmart are including low costs, high volume, increase customer satisfaction and expansion strategy. Walmart creates name recognition and customer satisfaction, and combined the retailer with the reputation of offering the best prices. They also expand their new business segments to different sectors such as pharmacies, automotive repair, and grocery sales to increase their sales revenue. Expansion strategy: The company realized that building a new store will allow for increase market share value. After their success in the rural areas, Walmart moved to urban areas and then moved to surrounding areas. The expansion strategy made Walmart the number one retail store in the United States. As Walmart continue its expansion domestically, the firm decided to go international. Furthermore, Walmart realized that acquiring an existing retail firm is necessary for expand domestic and international markets. Therefore, Walmart by acquire retail store which enable to expand locally and internationally. Always low prices make customers live better strategy is believed the strongest strategy used by Walmart. The firm developed the idea of dealing directly with the manufacturer and with the power control by Walmart will enable it to get the best deal from the manufacturers and suppliers. Organizational structure, culture, and control systems Saving people money to help them live better was the mission for Walmart. Hence, Walmart negotiates different suppliers and understanding their cost structure in order to reduce the price. Walmart has to be certain that the manufacturers were doing their best to cut down costs. Also, Walmart believed in establishing a long-term relationship with their suppliers. Walmart had 129 distribution centers located at different locations all over the US. Over 80,000 items were stocked in these centers. Walmarts own warehouses directly supplied 85 percent of the inventory, as compared to 50-65 % for competitors. Shipping costs for Walmart is about 3 % which is lower than its competitors, 5%. The distribution centers ensured a steady and consistent flow of products to support the supply function (Walmarts Cost Leadership Strategy, 2004). Walmarts logistics infrastructure was its fast and successful transportation system. The distribution centers were serviced by more than 3,500 company owned trucks. To make its distribution process more efficient, Walmart also uses a logistics technique called cross-docking. In this system, the finished goods were directly picked up from the manufacturing plant from suppliers, and then directly supplied to the customers. The system reduced the handling and storage of finished goods, eliminating the role of the distribution centers and stores (Walmarts Cost Leadership Strategy, 2004). SWOT Analysis for Walmart Store Inc. (S)trengths Reputation Brand Name: Walmart is a powerful brand and pioneer in the retail industry with the wide spread network of stores. It has a reputation for low price, convenience and a wide range of products all in one store for customers. Walmart has captured about 10% of the retail market in the U.S. and continues to expand. Walmart stores continue to open all over the country making Walmart a household name. Walmart has also been widely acknowledged for its social responsibility actions. The company has donated to a variety of charitable organizations and has been accredited for bringing jobs and wealth to less developed communities. Offer Low Prices: Walmart uses its enormous size and buying power to pressure its suppliers into extremely low prices, offering orders of high volumes of merchandise in exchange for low prices. The good thing about Walmart is that its shifts the low cost advantage to customers and available the products at lower prices. It has loyal customer base because it meets the expectation of customer by always delivering the goods at lower prices at compare to its competitors. Expand Global Market: Walmart has aggressively expands its international market over the past few years and has experienced global expansion. For example its purchase of the United Kingdom based retailer ASDA. Technology: Technology is strength to Walmart with its inventory control system that was recognized as the most sophisticated in retailing. The technology linked all the stores to the headquarters and the companys distribution centers. It also enables the warehouse of which the goods are ordered, and direct the flow of goods to the store and proper shelves. Supply chain and logistics management: Supply chain and logistics management are one of the strengths of Walmart. This allows Walmart to utilize the Just- in-time inventory concept and avoid the pilling up inventory to save the extra cost for maintaining inventories in the warehouses. Human Resource: Walmart always keen to provide training to their employees to improve the customer service level. The firm hire locally, provides training programs for its employees. Walmart also gets its employees involve and encourage them to make use of words like: we, us, and ours. It also provides stock ownership and profit sharing with great contribution from the H. R of the firm. Walmart was named one of the best 100 firms to work for. Cross-docking inventory system: Using the cross-dock technique, Walmart was able to effectively leverage their logistical volume into a core strategic competency. Walmart operates an extensive satellite network of distribution centers serviced by company owned trucks. Its satellite network sends point of sale (POS) data directly to 4,000 vendors. Each register is directly connected to a satellite system sending sales information to Walmarts headquarters and distribution centers. (W)eaknesses Employee turnover: Walmart has high employee turnover which costs more money and time for company to train the new employee. Bad publicity: Walmart is currently facing a gender discrimination lawsuit. Their female employees accuses that they were discriminated against in matters regarding pay and promotions. And also, Their female managers were accounted for the minority group in the company. Lock of flexibility: Walmart sell very wide range kinds of products for example like clothes, food, pharmacy or stationary which lack of flexibility compare with other more focused competitors. Other competitors may have the ability to make changes and improve on a certain product lines when the needs of their customers change. Walmart, however, may have too much merchandise and not be able to focus in on sectors that need to be improved. Some products have poor quality: Although Walmart provides low price of products, however, customers sometimes complain about the poor quality of few products. Facing difficulty in International market: It is hard for Walmart to expand their business out of US to totally different countries all around the world. Moreover, Walmart has to facing different culture and customer behavior in different countries, for example Walmart facing difficulty to expand the market in China. (O)pportunities Customers: Because Walmart provides low price to their customers, so they are able to attract more customers. Furthermore, customers basically are able to purchasing everything in one store that satisfied their needs. Walmart 24 hours stores also satisfied their customers. Diversified store types: Walmarts different store types and new locations provide more opportunities to exploit new market. Stores diversified from local, small-based sites to large super centers. International Expansion: No doubt that continued expand the international market is a huge opportunity for Walmart. Walmarts oversea stores have experienced significant growth. There are actually tremendous opportunities for future growth in developing countries and Asian markets than in the United States such as China and India. Creating strategic alliances and licensing agreements with other global retailers are ways to move into different countries. (T)hreats Competition: Walmart faces different strong competitions locally and internationally. Walmart main competitors are including Kmart, Target, Carrefour and Costco wholesale. In 2010, the Net Profit Margin for Walmart is 3.59%, Target 4.22%, Costco wholesale 1.69%, Carrefour 0.38%, respectively (Hoovers, 2010). Target is Walmarts direct competitor in the US, offering a range of general merchandise in a similar store format (Wikinvest, 2010). Economy Recession: The revenue for Walmart is affected by economy recession. Good economy is an opportunity for great business, because customers will have more money to spend. If the economy is great, there will be more jobs and people will shop more. However, if the economy is bad, there will be fewer jobs and people will shop less. Also, with the high price of gasoline and its effect on the economy, Walmart will certainly be affected the most. Strategy imitation: Walmart strengthens its competitive advantage on low-cost products. Other competitors may imitate their low-cost strategy to take over their market shares. Low Brand Loyalty: In the retail industry, customers would like to choose the product with the lowest price. In other words, customers do not care about the brand or which retail stores, if Costco has the exactly same chips that sell cheaper than in the Walmart, then customers will choose to buy the chips in the Costco not Walmart. TOWS MATRIX STRENGTHS WEAKNESSES Reputation Brand Name Bad publicity Offer Low Prices Lock of flexibility Expand Global Market Some products have poor quality Technology Facing difficulty in International market Supply chain and logistics management Employee turnover Human Resource Cross-docking inventory system OPPORTUNITIES OPPORTUNITIES-STRENGTHS OPPORTUNITIES-WEAKNESSES Customers Build on its already efficient distribution system to further expand in the U.S and globally. Walmart should be awareness and strict to control of the quality of the product in order to keep their customers basis. Diversified Store Types Expand diversified store types to International market in order to increase profit in International market. Set higher employment standards through enhanced training to keep their employees have best performance. International Expansion Duplicated the successful delivery logistic management and the distribution centers into International market. Continue to build on cost efficient pricing and production due to expansion. Go into new markets and buy out their local retailers to gain market share. THREATS THREATS-STRENGTHS THREATS-WEAKNESSES Competition Buy raw materials or products from local suppliers to hold a better political status within the local community further to compete with their competitors. Human resource department should set a benefits long-term promotion program or standard and training program for their employees in order to decrease the employee turnover. Economy Recession Create their own brand of products and increase the quality of products in order to establish customers loyalty. Establish joint venture partnerships or long-term relationship with local retail companies to get the advantages in the International segment. Strategy imitation Develop strong RD and technology to enhance the competitive advantage and avoid imitation from other competitors. Low Brand Loyalty Five Forces Analysis for Walmart Store Inc. Threat of entrances Low The threat of new entrance in the grocery and discount retailer industry is very low. New entrants have to face with the strong low-price competition among exist giant retail companies like Walmart, Costco and Target. New entrants need to invest large amount of capitals to establish their brand recognition, service, and variety of product offerings that Walmart, Target, and others competitors continue to improve on each day. In addition, existing companies can drop prices lower in order to force a new competitor out of the market. Therefore, the threat of entrances is low. Power of buyer-High Customers have many choosing opportunities and consider about products very details. They want the product now and they want it with the best service, best quality and reasonable price. Customers also enjoy increasing choice of products and choose one product that has the best quality and better price. For example, if customers find out Target sells an exactly product that has better quality and price than Walmart, and then they will choose to buy it in Target instead of Walmart. Power of Suppliers   Low The bargaining power of suppliers is very low. Walmart is very famous on giving pressure to their suppliers to cut their price lower and lower in order to offer the lowest price to their customers. On the other hand, become the supplier of Walmart is a very fierce competition. In 2004, about 10,000 new suppliers applied to become Walmart vendors. However, only about 200, or 2%, were ultimately accepted by Walmart (Gwendolyn Bounds, The Wall Street Journal). Therefore, the bargaining power of suppliers is low. Rivalry High The competition in the US grocery and discount retailer industry is very high. The main competitors for Walmart in the local market are Kmart and Target. These companies also have to face competition from wholesalers such as BJs, Costco and even the international market such as Carrefour. Walmart has adopted a cost leadership generic strategy. In the past, most companies have not been able to match Walmarts strategy everyday low prices. However, Walmarts barrier to entry (economies of scale) and strength (supply-chain management) can be easily imitated with sufficient resources. Therefore, retailers are in a fierce competition that see who can offer their customers the lowest price. Threat of substitute Low The threat of substitutes in this industry is low because only few companies have ability to offer such a variety of products available instantly and also low prices. One possible substitute is online shopping; however, customers usually do the online shopping for clothes or other stuffs but not for food or grocery shopping. Therefore, the threat of substitute is low. Key Strategic Issues Issue #1: Open too many new stores close to existing stores lead to new stores taking over the market shares from existing stores. Status Quo Wal- Mart depends on opens many new stores and expands into new market to increase the long-term sales and income growth. However, because of Walmarts large size of expansion, new stores are effects the sales on existing stores. For example, Walmart builds a store relatively close to an already existing store, the new store might take away customers from the old store thus decrease the sales in existing stores (Walmart, 2010). Evolutionary Change (Incremental Improvement) In order to solve this problem, Walmart expands their business segment into international market instead of domestic market. For example, Walmart opened 5 times number of stores in the international market in 2010 compared to domestic stores; most of stores are in Mexico, China, and Central America (Walmart, 2010). Revolutionary Change (Huge/Drastic Change) Walmart is also aggressively to open business segments in India if the country opens up the sector to foreign direct investment. India has retail market more than 1 billion; no doubt India is a huge opportunity for Walmart. However, retailers that carry multiple brands (like Walmart) are restricted to wholesale outlets in India. After Indias policy change, Walmart is allowed to expand superstores and generate revenue in India (Walmart, 2010). Specific tactics to implement the strategy Walmart needs to establish long-term relationship or joint venture with local retail company to get into the market in India. Although in 2006, Walmart announced that it had tied up with Bharti Enterprises Ltd. (Bharti) to get into the Indian retail sector. Bharti was a diversified company, and one of the biggest mobile telephone service providers in India (Walmart and the Indian Retail Sector, 2007). However, because of the government policy, the small retailers groups and the Left parties against allowing the company into India are all the barriers that Walmart has to face it. Issue #2: International competitors Status Quo In order to expand and improve the sales revenue for the economy recession especially in the domestic market, Walmart has been aggressively expand its business segment into international market. However, the local big retailers or small retailers groups are against Walmart to get into their market to take over the market shares because of its low price strategy (Walmart, 2010). Evolutionary Change (Incremental Improvement) Improve its supply chain, logistic and technology segment to lower its delivery and operation costs in order to compete with local big retailers such as Britains Tesco, Frances Carrefour, and Germanys Metro (Walmart, 2010). On the other hand, retail business segment is hard to create products differentiation, because commodity products are all the same for customers. The only way that gains the market shares for retail stores is not only low price but also quality of products. Therefore, Walmart should awareness of its quality of products to attract more customers even in the international market. Revolutionary Change (Huge/Drastic Change) Walmart should acquire and purchase the local retail companies in order to get into the international market. On the other hand, establish long-term relationship with local suppliers to have the win-win situation for their cooperation. Specific tactics to implement the strategy In the beginning of year 1, 2 and 3, Walmart should first focus on improving its supply chain, logistic and technology improvement in order to compete with local big retailers on its lower operation, delivery costs and high quality of products. For the long-term tactics, Walmart should deeply penetrate into the local market, understand different cultures and customers behaviors and then cooperate with local suppliers to establish long-term partnership. Personal assessment SWOT Analysis of myself in relation to the organization (What can I offer to the organization?). (S)trengths: International expansion (China): Walmart is extremely aggressively penetrated into the market in China. Also, no doubt that China has 1.3 billion populations which accounted for the most majority population in the world, creates a huge business opportunity for Walmart. Therefore, Walmart needs a manager who can speak fluently Mandarin and English, and really understand about Chinese culture and Chinese customers behavior. Hence, I can offer Walmart my knowledge to develop more opportunity in Chinas market in order to maximize the profits. (W)eaknesses: Lack of working experience: Even though I can speak fluently Mandarin and understand the Chinese culture and customers behavior; however, I still lack of working experiences. I do have some part time working experience such as working in starbucks, but do not have full time working experiences. (O)pportunities: Because of my professional knowledge (bachelor and master degree are both business management) are expertise on this field which can offer Walmart a professional employees or manager. Moreover, my family also has business in China, Hangchow, which makes me has understanding and interested about China. I can provide Walmart establish partnership with local suppliers and establish long-term relationship with them to compete with local retails competitors. (T)hreats: Many applicants around the world: There is still having many talented applicants around the world apply to get into this company. Some of the applicants have high education degree and business knowledge and also have ability to speak many different kinds of languages. Therefore, I am in extremely fierce competition. Not every business segment in Walmart is my expertise: I have weakened and lower advantages compared to local American because of the speaking and cultural differences. Furthermore, the company does business in many different retail formats, including supercenters, food and drugs, general merchandise stores, cash and carry stores, membership warehouse clubs, apparel stores, soft discount stores and restaurants. However, not every business segment in Walmart is in my field of expertise. Financial Analysis 2010 Annual Sales (Figure2-1) (Source: Hoovers, 2011, http://0subscriber.hoovers.com.leopac.ulv.edu/H/company360/competitiveLandscape.html?companyId=11600000000000) As you can see in Figure 2-1, this is 2010 annual sales for 4 main retail stores in the United States. They are including Walmart, Target, Costco Wholesale and Carrefour. Walmart has almost $400 billion sales in 2010. Compared to other competitors, annual sales for Walmart was much higher than other companies. Carrefour annual sale in 2010 was around $100 billion. Annual sales for Target and Costco were just around $50 billion in 2010. 2010 Net Profit Margin (Figure2-2) (Source: Hoovers, 2011, http://0subscriber.hoovers.com.leopac.ulv.edu/H/company360/competitiveLandscape.html?companyId=11600000000000) In Figure 2-2, net profit margin in 2010 for Walmart was 2.98%. Target was higher than Walmart which had 3.69% net profit margin in 2010. Other two competitors, Costco and Carrefour were both under 1.84% in net profit margin in 2010. Figure 2-3 (Source: Hoovers, 2011, http://0subscriber.hoovers.com.leopac.ulv.edu/H/company360/competitiveLandscape.html?companyId=11600000000000) The Return on Asset ratio is useful in measuring how efficiently a company uses its assets to generate profit. By definition, ROA is calculated by dividing the Net Income by the total asset of a company. Refer to Figure 2-3, ROA for Walmart from 2006 to 2010 are much higher than its competitors. Walmarts ROA were around 9% to 10% each year, compared to its competitors which were all much lower than Walmart. This basically means that Walmart utilizes its assets well enough to generate profit in comparison with their competitors. However, ROA in 2007 for Target is higher than Walmart, Target 9.29%, Walmart 9.05%. Targets major competitive advantage over Walmart lies in its customer base: the average household income for Target customers is about $50,000 a year, whereas the average yearly income for a Walmart customer is only $35,000 Figure 2-4 (Source: Hoovers, 2011, http://0subscriber.hoovers.com.leopac.ulv.edu/H/company360/competitiveLandscape.html?companyId=11600000000000) The return on Stockholders Equity (ROE) ratio measures the percentage of profit earned on stockholders investment in the company. In other words, return on equity  measures a corporations profitability  by revealing how much  profit a company generates  with the money shareholders have invested.  Ãƒâ€šÃ‚   In Figure 2-4, ROE for Walmart were around 20% from year 2006 to 2010, compared to other competitors which are higher than others. Figure 2-5 (Source: Hoovers, 2011, http://0subscriber.hoovers.com.leopac.ulv.edu/H/company360/competitiveLandscape.html?companyId=11600000000000) Net profit Margin is an indication of how effective a company at cost control

Cultural Impact of Hinduism in India Essay -- Cause Effect India Essay

Cultural Impact of Hinduism in India Huge population, pollution, peace, snakes, saris, dance, curry, and religion are probably the most popular words that come up when we think about India. India is a well-known country. Although it is a relatively poor country, it has a rich and diverse culture. India is populated by approximately 953 million people. It has been a home for many religions, including Hinduism, Buddhism, Sikhism, Jainism, Islam and Christianity. The first four mentioned above originated in India (Finegan 151). Seventy percent of the populations are Hindus. In fact, Hinduism is the oldest and third largest religion in the world. Hinduism has deeply influenced Indian society, for several reasons: it has a long history in this place, it is related to the social status of Indians, and it is integrated with the cultural aspects in India. Hinduism has faith in the deity that is visualized in a triad. They are Brahma, the creator; Vishnu, the preserver; and Siva, the destroyer. The article about Hinduism in the www.religioustolerance.org states that there are thousands of gods that Hindus believe, but generally there are two major sects in Hinduism; the Vaishnavaism – esteems Vishnu as the supreme god, and Shivaism – esteems Siva as the supreme god. Like other religions, Hinduism has books that give its followers directions for living. They are the Vedas and Upanishads, and epics such as Mahabarata, Ramayana, and Bhagwad Gita. The Vedas is the most fundamental book for Hindus. It was dictated by the god Syva's son, named Ganesha. In Hinduism, the basic goals of life called purushartha or "The Four Ends of Man," include dharma (acting righteously and fulfilling one's duties), artha (money, prosperity, or wealth... ... Because of its long tradition, its links to social status and its integration with cultural activities, Hinduism has had an enormous influence on Indian society. It is not only a religion, but also a way of life for Indians. Works Cited Chopra, P.N., ed. Religions and Communities of India. Atlantic Highlands, NJ: Humanities, 1982. "Culture". Welcome to India (1999). 8 Mar. 2000 <http://www.welcometoindia.com/home.html>. de Bary, Wm. Theodore, ed. Sources of Indian Tradition. New York: Columbia UP, 1960. Finegan, Jack. India Today! St. Louis, Missouri: The Bethany Press, 1955. "Hinduism". Religious Tolerance Organization (1999). 8 Mar.2000 <http://www.religioustolerance.org/hinduism.htm>. Sharma, Arvind, ed. Our Religions. San Francisco: Harper Collins Publishers, 1995. Weber, Max. The Religion of India. Illinois: Glencoe, 1962.

Lolita Essay -- Literary Analysis, Vladimir Nabokov

A Love Story: Or Is It? â€Å"Lolita, light of my life, fire of my loins, my sin, my soul† (Nabokov 9). Quoted from Vladimir Nabokov’s novel Lolita, Humbert Humbert briefly describes his sensibilities towards his love Lolita. I’ve italicized love for the reason that this book is perceived often as not a true American love story but as a pedophile’s lust. The reasoning for the italicization is because I wanted to emphasis on the point that this book offers more than that of a pedophile’s love. Nabokov’s novel does a very good job of creating an interesting yet unorthodoxed plot. What Nabokov might find acceptable in today’s society, some people might find very offensive and disrupting. He does this to grab the reader’s attention; therefore, building their interests by having them see the other side of things. Why many readers may find this book to be associated with pornography or just another literary piece surrounded around pedophilia, Nabokov hits you with textual evidence, which may sway reader’s minds. As a reader of this novel, I am compelled to show you how this book is a true American â€Å"Love Story.† Before I move on, I’d like to actually show you that Humbert is indeed considered a pedophile and let’s not forget this. Gunter Schmidt, once wrote that: Pedophiles are men whose sexual wishes and desires for relationship bonds and love are focused either primarily or exclusively on children who have not reached puberty, whereby the relative importance of each of these three areas—sexuality, relationship, and love— may vary, as it does with other people as well. (Schmidt 473) As you can see, Schmidt has laid out a very narrow and precise definition of a pedophile. With only reading the first line, I noticed that auto... ...articles written by knowledgeable researches that help back up my argument. I agree that one may say that Humbert was a pedophile but people can change in flash and I feel that the evidence I have provide you with is substantial. Humbert isn’t a bad guy in the least bit, he’s just confused but then soon finds reality. Lolita was the best medicine for him. Lolita’s actions towards Humbert somewhat pushed him away from the pedophile stage because Humbert felt that he was the one being used. She was very mature than the other prospected nymphets, which had a great impact on him. Humbert has feared love his entire life until Lo enters his life. This novel may have some disturbing and gruesome parts but this just leads to the ultimate goal and that’s love. With evidence provided, Nabokov’s stylish, high-toned story is the greatest love story every published.

Arts and Politics :: Arts Politics Essays

Arts and Politics Many artists tend to overwork themselves and get frustrated. Even if they were to work 18 hours a day on a project, many of them still wouldn’t be able to pat themselves on the shoulder and say â€Å"You’ve done a great job! You should be happy!† Of all the times they say it, they rarely feel it. But when you get down to the bottom of it, they absolutely love to do it! Just like that, Emilie gets neurotic when she works, and hence she prefers to work alone at night. Upon my insistence she reveals some of the secret characteristics of her artist persona: she gazes at the drawing, moves back, gazes some more, moves closer, speaks to herself and works for long hours until the picture in her head comes alive. It’s rarely the exact same picture, but sometimes it’s â€Å"even better†. She’s sharing her studio with two friends this year. As a result, she prefers to work at home even though she misses the times when she pulled all-nighters in Johnson and turned the lights off as the sun came up. Even though she works like a zombie in order to avoid human presence, the result inevitably begs for the daylights due to its political subject matter. Emilie doesn’t do art for art’s sake. She has a purpose: to make the viewer think. â€Å"It’s really important to know what you think about everything.. ‘Cause if you don’t know, then what are you bringing to the table?.. In order to be a complete person you need to have complete opinions about a wide spectrum of things.† Unlike some political art that’s shoved into your face, Emilie tries to be subtle. She likes to put things out there and leave the interpretation to the viewer. Her work mainly deals with gender, popular culture, and western mentality. â€Å"I’m a woman, I’m American, I’m middle upper class, I’m a consumer, I’m privileged. But I’m also influenced by the struggles of the other class-lower class†¦ I’m fascinated by the concept of the exotic and how western communities turn that into a commodity. The consumption by the west of the east..† she says as she sips on Red Bull, takes another drag from her cigarette, the count of which I lost a while ago, and puts on some techno/pop music on the computer. But that’s not all. Despite her interest and respect in political art, she immediately sits up when asked her favorite artist and speaks the name James Turrell.

Feminism Ophelia Hamlet

Aphelion's struggles in the patriarchal society in which she lives and the loss of her identity as a whole, by not only her father, but other authorial males in her life. Throughout the beginning of the play, Aphelia, is used as somewhat of a pawn by all the male figures in her life, emotionally, physically, and even for sheer politics.Her lack of a mother figure and severe dependence on her father and brother, as well as other males, has literally taken away who she really is, her opportunity to make and act on her own decisions. Aphelia is treated by her father as if she is not only his daughter, but his possession. When Aphelia first speaks to her father about Hamlet, he states â€Å"l do not know, my lord, what I should think,† (1. 2). Polonium responds in an authoritative way, basically attesting himself as the decision maker. When he states â€Å"You do not understand yourself so clearly.. â€Å"(l . 3), he attacks her competence to handle herself. He goes on to say â €˜â€ ¦As it behooves my daughter and your honor†(l . 3), making it clear that it would be in her best interest to behave according to the â€Å"set† standards and how she acts and presents herself, reflects onto him as her father and as a member of the kings court. It is clear he doesn't care for Hamlet and ants his daughter to have nothing to do with him, convincing her that she is nothing to him.. But, after hearing more about Hamlet acted towards her by grabbing her and just staring into her, he takes full advantage of the situation and instructs his daughter to behave according to his best interests, to get closer to the king, Claudia.Aphelia, living in a male dominated world, has over the years, lost herself as a person, as a woman, doing things that she wouldn't normally do, such as be a part of her fathers plan to expose Hamlets reason for his â€Å"madness. † During the time when the play was written, women were marginalia, often dewed as property, even with fathers and daughters. In that society a woman would be required to be a dutiful daughter, wife, and mother, and dare not stray away from those approved roles that were placed upon them. Aphelia, growing up always being the dutiful daughter, obeys her fathers wishes and follows through with the plan.The pitfalls to being a dutiful daughter, in her case, is that she lost the one man that made her happy, her lover, not only says horrid remarks to her, but breaks her down, and any little bit of â€Å"reality' she had was lost forever. The hazards of being a dutiful daughter/mother/wife, are always present. There is the immediate consequences, then there are the ones that over time, as her character â€Å"screams† out to the audience, being oneself becomes obsolete. Her brother, Alerter, who is going back to France, also â€Å"advises† his sister to keep away from Hamlet.Expressing that Hamlet being a prince, would marry for the good of the state and due to the differences in class, Hamlet would not marry Aphelia. Alerter also believes that Hamlet cares for her but â€Å"loves† her only for sexual need. â€Å"Forward, not permanent, sweet, not lasting. † (1. 3). Unfortunately, exposing another â€Å"role† a woman would face in a patriarchal society, sexual roles. He is also concerned with her good name and family reputation, possibly implying that she could get pregnant and he would leave her, thus putting herself in a â€Å"unacceptable† role of a woman; a woman with a past, forever branding her and the family name.Hamlet plays on her emotional strings. He has expressed his love for her and has given her gifts. The sudden death of his father and finding out the reasons behind his death, as well as the disgust of his mother marrying so quickly afterwards, molds Hamlet too man he has never been before; untrusting, and very paranoid about others close to him, and for very good reason. He took out his anger with hi s mother on all who loved him. His only life line was Aphelia, the only one he thought of as true, or tried to make himself believe that she was, by grabbing her and observing her closely, as if he could see right through her.After her ultimate betrayal, by setting him up and lying to him about where her father was, she, cut off his life line. By doing so, he insults her, tells her that he loved her once, and belittles her to no end, until she is ambushed by so many emotions, that she is left in total confusion and heartbreak. With her brother in France, Hamlet rejecting their relationship, Aphelia finds out ere father has been killed by Hamlet. She in a sense, is left â€Å"alone,† and cannot handle herself, without the direction of her father , brother and Hamlet.At this point its clear, Aphelia has gone totally mad, Speaking very little, and if anything it is about her deceased father in chants and song. Now with the males in her life are gone, she has served her purpose i n the story. She starts going down a downward spiral and shortly thereafter, she commits suicide, or at least it was implied that she did, by drowning. In conclusion, although a small, seemingly insignificant character, Aphelia, not only provides the reader to the philanthropic ideals and patriarchal attitudes towards women.But also serves to be somewhat like a mirror to the audience, one by one, â€Å"reflecting† the characters true self/intentions. Maybe being her ONLY purpose in the story to unveil her co-characters motive and who they really are in general. Polonium, her father, uses his daughter as some sort for property, for political gain and interest. Her brother, Alerter, again uses her for political reasons, somewhat, and to protect his name, uses her for the sake of his pride, and introduces sex, as Hamlets true goal with Aphelia.Then Hamlet himself, takes her on a reallocates of love/hate and confusion, labeling her as untrustworthy and corrupt, he destroys her em otional being, rendering her completely helpless and incompetent to handle life on her own. This view was the norm at the time, that many men saw as being true, that a woman will be nothing without a man but also fail to realize that without women men would hardly be anything as well, they need women, as shown in the play, to succeed in their own personal goals, whether financial, political or other. [1180]