Impact of Human Activities on Natural Hazards Essay Example for Free

Impact of Human Activities on Natural Hazards Essay Natural hazards are naturally occurring phenomena that have disastrous impact on humanity. These phenomena had been in existence even before the advent of humanity. The hazardous dimension of these natural phenomena are in the context of the impact that such a phenomenon would have on human population in the area affected by that phenomenon. In this essay, the effect that human activity has on these natural hazards would be analyzed. Some human activities may be exacerbating the factors that cause the natural hazard, like the impact of excessive and unplanned logging on floods and droughts. In certain other cases the human activities may cause subsequent or supplementary hazards to a primary hazard event, like building dams in earthquake prone zones may lead to flash floods and landslides in the event of a rupture. A hazard can be defined as an event that has the potential to cause harm. This potential may be on account of its unexpected timing of occurrence or the actual intensity of the event itself. Human societies can withstand these events within a normal scale of occurrence. However, human societies become vulnerable when these events occur unexpectedly or are of an intensity or duration that falls beyond that normal scale (O’Hare and Rivas, 2005). Natural hazards can be broadly classified under the heads of geological, hydrological, climatic and diseases. This essay would limit its scope to analyzing causal relationships, if any, of human activities on landslides, floods and drought and the secondary hazards triggered by those activities in the event of an earthquake. Of all human activities that have a direct or indirect impact on natural hazards, deforestation is by far the most significant. Deforestation is the removal or destruction of forest cover of an area. It may occur due to unscientific logging practices without regeneration and may be accompanied by subsequent conversion to non-forest usage like agriculture, pasture, urban, mining or industrial development, fallow or wetland. At a very broad level, it has been argued that deforestation is a major cause of global climatic changes. It has been predicted that removal of forest cover will lead to violent and unpredictable environmental fluctuations. At a smaller landscape, deforestation has a direct bearing upon the climatic, hydrological, edaphic and biological aspects of that area. Deforestation is associated with higher levels of soil erosion and landslides, sedimentation in river beds and changes in fluvial geomorphology (Haigh, 1984). Quite a few of these effects of deforestation have a direct bearing on the natural hazards that will be covered in this essay. One of the major functions of a forest is to maintain the humidity level in the atmosphere. Trees withdraw groundwater through their roots and transpire the excess water through their leaves. Forests return a major part of the rainfall received by them through evapotranspiration. Annual evapotranspiration in tropical moist lowland forests ranges up to 1500 mm per year, with transpiration accounting for a maximum of 1045 mm per year (Bruijnzeel, 1990). This process of evapotranspiration in the leaves of trees takes the latent heat of evaporation from the surrounding atmosphere. Thus evapotranspiration has a cooling effect on the atmosphere that aids precipitation. Deforestation denies the atmosphere of this cooling effect and is thus a contributing factor to lowering of annual rainfall in an area. Further, the effects of deforestation generally compound the severity of drought. Lack of trees translates to the lack of root fibers that hold the topsoil. In the event of a drought, the topsoil flakes and gets blown by the wind, leading to severe dust storms. This phenomenon had devastated the American Great Plains for close to a decade in 1930s. The dust bowl covered farming areas in Colorado, Kansas, north west Oklahoma, north Texas and north east New Mexico. The fertile soil of the plains was exposed due to lack of vegetation cover and actions of the plow. These farming techniques that led to severe soil erosion, coupled with prolonged periods of extremely low rainfall, led to a series of severe dust storms that ranged up to the Atlantic coast. Much of the fertile topsoil was lost in the Atlantic (Cartensen et al. , 1999). Direct causal relationship between human activity and drought is yet to be conclusively established. However, there are studies available that point to a positive correlation between the two. For example, climate-modeling studies have indicated that the 20th century Sahel drought was caused by changing sea surface temperatures. These changes were due to a combination of natural variability and human induced atmospheric changes. The anthropogenic factors in this case were rise in greenhouse gas levels and aerosols (GFDL Climate Modeling Research Highlights, 2007). The effect of human activities like deforestation is rather more direct and pronounced in case of hydrological hazards like fluvial floods. Fluvial floods occur when the discharge of a river exceeds its bankfull capacity. Forests create deep, open textured soils that can hold large quantities of water. When the forest cover is removed through logging, the soil becomes compacted. More rainwater is converted to runoff or near surface flow and less proportion percolates as groundwater. Research has shown significant increase in monthly runoff following logging activities (Rahim and Harding, 1993). The runoff rainwater carries with it considerable amounts of loose soil particles. Removal of vegetation cover through excessive logging activities or overgrazing leaves the soil bare. In such a situation, the upper layer of the soils becomes susceptible to erosion by surface runoff. These suspended soil particles are deposited on the riverbeds. The effect of this type of soil erosion by surface runoff is even more pronounced when the deforestation happens in the riparian zones as well.

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, <

Clinico-histopathological Spectrum of Cutaneous Vasculitis

Clinico-histopathological Spectrum of Cutaneous Vasculitis Article Type: Original Title: Clinico-histopathological Spectrum of Cutaneous Vasculitis: A Retrospective Study of 62 cases Running Title: A Clinico-pathological study of Cutaneous vasculitis Authors: Nadia Shirazi*, Rashmi Jindal^, Neha Tyagi*, Samarjit Roy^, Meena Harsh,* Sohaib AhmadÇ‚ Affiliation: Department of *Pathology, ^Dermatology and Ç‚Internal Medicine, Himalayan Institute of Medical Sciences. SRH University. Jolly Grant. Dehradun. Uttarakhand. India Corresponding Author: Dr. Nadia Shirazi ABSTRACT Context: Cutaneous Vasculitis is the inflammation of vessel walls which leads to hemorrhagic or ischemic events. The histopathological classification of cutaneous vasculitis depends on the vessel size and the dominant immune cell mediating the inflammation. Object: We studied the etiological factors and clinico-pathological spectrum of patients with cutaneous vasculitis at a tertiary referral centre of north India. Design: Skin biopsies of all patients with clinically suspected cutaneous vasculitis presenting over 5 years , between 2009-2014 were reviewed. Cutaneous vasculitis was classified on the basis of etiology (primary or secondary), on the basis of size of vessel wall as well as on the dominant inflammatory cell infiltrating the vessels. Results: Over 5 years, 62 / 103 patients evaluated for vasculitic syndromes had histologically proven vasculitis. Clinically, vasculitis was primary (77.4%) or secondary (22.5%) to drugs, infections, underlying connective tissue diseases and malignancy. Neutrophilic (n=30), lymphocytic (n=18), eosinophilic (n=10), and granulomatous (n=4) vasculitis were the major histopathological groups. Small vessel involvement was seen in 97% cases. Conclusion: Skin biopsy remains the gold standard for diagnosing cutaneous vasculitis. Small vessel vasculitis is the most common type of cutaneous vasculitis with the dominant cell type being neutrophilic. Eosinophilic infiltrate was exclusively associated with primary vasculitis. Keywords: Cutaneous vasculitis, Small vessel vasculitis, Skin biopsy INTRODUCTION Cutaneous vasculitis (CV) is an inflammatory process of the vessels leading to the destruction of their wall with subsequent hemorrhagic features with or without ischemic necrosis.1 The incidence of cutaneous vasculitis ranges from 15.4 to 29.7 cases per million per year.2,3 The condition usually affects adults with a slight female predominance, however, all ages may be afflicted. CV is classified histo-morphologically on the basis of size of vessel affected (small or medium vessel vasculitis) and on the basis of the dominant cell mediating inflammation- neutrophilic/leukocytoclastic, lymphocytic, eosinophilic and granulomatous. On the basis of etiology, they are classified as primary/idiopathic or secondary to an underlying cause like drug induced, connective tissue disorders, infections, malignancy, etc. Vasculitis in a medium or large vessel is defined as presence of inflammatory cells within their walls, whereas in small vessels diapedesis of various leukocytes often take place and this criteria alone is not significant. It must be associated with signs of vessel damage, such as fibrin within the walls, thrombi or endothelial necrosis. Veins are involved more commonly than arterioles. Clinically, CV can present with a variety of signs and symptoms like urticaria, palpable purpura, ulcers, maculopapular rash, nodules, hemorrhagic vesicles, etc. It can be limited to skin or manifest in other organs like kidney, lungs and heart. Due to this myriad of presentations, CV can mimic a variety of other dermatological and systemic diseases. Skin biopsy remains the gold standard for diagnosis of cutaneous vasculitis complemented by clinical data and relevant haematological and immunological investigations. In this article, we will be presenting the histopathological spectrum of cutaneous vasc ulitis at a single centre of north India. MATERIALS AND METHODS All patients with clinical suspicion of cutaneous vasculitis attending the dermatology OPD between August 2009 and July 2014 at a single tertiary referral centre of north India were included. An informed consent was taken wherever possible in writing. Approval was obtained from the institute’s research committee for compiling the data from the hospital records. A punch biopsy, 4mm in depth was taken from the edge of the lesion. Though efforts were made to collect most of the biopsies within 48 hours of appearance of the suspected vasculitic lesion, a few patients presented as late as 1 -2 weeks. These biopsies were routinely processed and stained with Haematoxylin and Eosin (HE). Serial sections were taken in which no vasculitis was identified on initial section. Elastic tissue staining to assess the damage to the elastic lamina in muscular vessels was also performed. Simultaneously, a hemogram, ESR, kidney and liver functions, rheumatoid factor and immunological tests like AN A and ANCA were also carried out for assessment. Direct immunoflourescence (DIF) could not be undertaken in any case due to poor patient affordability and lack of infrastructure. Patients with thrombocytopenia ( RESULTS Over 5 years a total of 480 skin biopsies were studied out of which 103 cases were performed in those with clinically suspected vasculitis. However, 62 out of these 103 cases were histologically confirmed to have vasculitis; the remaining had unremarkable and non-specific histologic features. Those with positive histological features had a mean age of 44.5 years [range 6-83 years] with the male to female ratio of 1.1:1. The maximum number of patients (n=15) were seen in the age group 31-40 years followed by those in the second decade. Clinically vasculitis was primary (n=48, 77.4%) or secondary (n=14; 22.5%). (Table I) History of drug intake and presence of recent upper respiratory tract infection was seen in 7 and 3 patients respectively. The commonest offending drugs were antibiotics of ÃŽ ²-lactam group and analgesics followed by anti-histaminics. Connective tissue disorders (n=3) and malignancy (n=1) were also found to be the cause of secondary vasculitis. Clinically palpable purpura was the most common finding followed by maculopapular rash.(Figure I). Three-quarters of granulomatous vasculitis presented clinically with symptoms of allergic granulomatosis; 25% (n=4/17) of leukocytoclastic vasculitis presented clinically with features of microscopic polyangiitis. Among the haematological parameters, a raised ESR was the most consistent finding. (Tables II III). Most of these were small vessel (venules and arterioles) vasculitis (n=60, 97%). Only 2 cases showed medium vessel vasculitis particularly associated with panniculitis. Depending upon the dominant cell mediating inflammation, the dominant cell type was neutrophilic (n=30), lymphocytic (n=18), eosinophilic (n=10), and granulomatous (n=4). Histopathological evaluation in neutrophilic vasculitis showed transmural infiltration of vessel wall with neutrophils (Figure II). Fibrinoid necrosis, neutrophilic debris with or without extravasated red cells were features of leucocytoclastic vasculitis. Lymphocytic vasculitis is shown in Figure III. Epithelioid granulomas were seen surrounding and destroying the vessel wall in granulomatous vasculitis with transmural vessel wall infiltration by lymphocytes and polymorphs (Figure IV). Medium vessel vasculitis showed infiltration by neutrophils in vessel wall which was associated with septal panniculitis. (Figure V). Six of the 10 cases with urticarial vasculitis had an eosinophilic infiltrate; the remaining showed lymphocytes predominantly. Clinically most cases (n=8; 47%) of idiopathic vasculitis were of neutrophilic type. Drug reaction was the commonest cause of secondary vasculitis (n=7) and most of these (n=4, 57%) showed lymphocytic infiltrate (Table IV). DISCUSSION Cutaneous vasculitis presents as a mosaic of clinical and histological findings due to varied pathogenic mechanisms.3 Even in the presence of suggestive dermatological lesions, biopsy showed histological features in nearly 60% cases. We observed primary vasculitic syndromes leading to cutaneous histologic changes in 77% of all cases. Joint pain and swelling was the main presenting feature, palpable purpura and maculopapular rash were the predominant clinical cutaneous markers and raised ESR was a consistent feature. Mostly small vessels were affected and neutrophils predominated in infiltrates. However, there was a substantial overlap in the calibre of the vessel, the cellular infiltrate and the clinical diagnosis. Our observations corroborate with the case series of Carlson et al in terms of the dominance of primary vasculitis and lack of organ involvement.3 Raised ESR was also observed by Ekenstam et al and Gupta et al.4, 5 Arthralgia was the commonest systemic manifestation also observed by Gupta et al. 5 Neural and renal involvement was seen in 15 (24.1%) and 18 (29%) patients respectively in our series. Earlier studies showed visceral involvement is seen in 6, 7, 8 Fatal disease occurs in a minority (3, 8 Different therapeutic approaches are the main reason for sub-classifying vasculitis. Avoidance or treatment of the causative factor may cure or limit the activity of secondary vasculitis; whereas immunosuppressive therapy is the treatment of choice for primary vasculitis. Given this broad range of presentations of cutaneous vasculitis and the numerous disorders that can mimic vasculitis, it is not surprising that it is difficult to correctly and confidently classify these patients. 9 Currently the most widely adopted vasculitis classification system is that of Chapel Hill Consensus Conference (CHCC) which is based on pathologic criteria . 10 The other widely used system is that of the American College of Rheumatology (ACR) which is based on clinical findings. 11-18 As yet, no ideal system of classification exists for vasculitis. 3, 19, 20 The most accepted classification is one which distinguishes between primary and secondary vasculitis, recognizes the dominant blood vessel size involved as well as incorporates patho-physiological markers such as direct immune-fluorescence (DIF) and ANCA.21,22 Therefore the classification of cutaneous vasculitis into specific syndromes is best first approached morphologically by determining vessel size and principal inflammatory response. 3 This is the first case series classifying cutaneous vasculitis based on the vessel calibre and histo-morphologic features from the north Indian state of Uttarakhand. Though, the referral centre caters to a million people, this data cannot be extrapolated to the general population as the people are treated in the periphery by practitioners, the data of which is non-existent. A major limitation of our study was the non-availability of direct immunofluorescence which is considered very important for delineating the immunoglubulin type. Nevertheless, since this facility is not available in most of the Indian subcontinent and there is a lack of expertise in the field of dermatopathology, our data merits attention. CONCLUSION Vasculitis occurs as a primary disorder or secondary to various medical conditions, the treatment differing accordingly. The severity may range from a self-limited condition to a life threatening disorder with multiple organ failure. Skin biopsy is an important tool in arriving at a definitive diagnosis duly complemented by clinical features, pertinent laboratory data, serological evaluation, ANCA with or without direct immunofluorescence. REFERENCES 1. Carlson JA, Cavaliere LE, Grant-Kels JM. Cutaneous Vasculitis: diagnosis and management. Clin Dermatol 2006; 24: 414-29. 2. Chen KR, Carlson JA. Clinical approach to cutaneous vasculitis. Am. J Clin Dermatol 2008; 9: 71-92. 3. Carlson JA, Ng BT, Chen KR. Cutaneous vasculitis update: diagnostic criteria, classification, epidemiology, etiology, pathogenesis, evaluation and prognosis. Am J Dermatopathol 2005; 27 (6): 504-28. 4. Ekenstam E, Callen JP. Cutaneous leukocytoclastic vasculitis-clinical and laboratory features of 82 patients seen in private practice. Arch Dermatol 1984;120: 484-9 5. Gupta S, Handa S, Kanwar AJ, Radotra BD, Minz RW. Cutaneous Vasculitides: Clinico-pathological correlation. Indian J Dermatol Venereol Leprol 2009;75:356-62 6. Fiorentino DF. Cutaneous Vasculitis. J Am Acad Dermatol 2003; 48(3): 311-40 7. Carlson JA, Chen KR. Cutaneous vasculitis update: small vessel neutrophilic vasculitis syndromes. Am J Dermatopathol 2006; 28(6): 486-506 8. Tai YJ, Chang AH, Williams RA et al. Retrospective analysis of adult patients with cutaneous leukocytoclastic vasculitis. Australas J Dermatol 2006; 47(2): 92-6 9. Carlson JA, Chen KR. Cutaneous pseudovasculitis. Am J Dermatopathol 2007; 29(1): 44-55. 10. Jennette JC, Falk RJ, Andrassy K et al. Nomenclature of systemic vasculitides: proposal of an international consensus conference. Arthritis Rheum 1990; 33 (8): 1135-6. 11. Fries JF, Hunder GG, Bloch DA et al. The American college of Rheumatology 1990 criteria for the classification of vasculitis: Summary. Arthritis Rheum 1990, 33(8):1135-6. 12. Leavitt Ry, Fauci AS, Bloch DA et al. The American college of Rheumatology 1990 criteria for the classification of wegener’s granulomatosis.1990;33(8):1101-7 13. Masi AT, Hunder GG, Lie JT, et al. The American College of Rheumatology 1990 criteria for the classification of Churg-Strauss Syndrome (allergic granulomatosis and angitis). Arthritis Rheum 1990; 33(8): 1094-100 14. Hunder GG, Bloch DA, et al. The American College of Rheumatology 1990 criteria for the classification of giant cell arteritis. Arthritis Rheum 1990; 33(8): 1122-8 15. Mills JA, Michel BA, Bloch DA et al. The American College of Rheumatology 1990 criteria for the classification of Henoch-Schonlein purpura. Arthritis Rheum 1990; 33(8): 1114-21. 16. Calabrese LH, Michel BA, Bloch DA et al. The American College of Rheumatology 1990 criteria for the classification of hypersensitivity vasculitis. Arthritis Rheum 1990; 33(8): 1108-13. 17. Lightfoot Jr RW, Michel BA, Bloch DA et al. The American College of Rheumatology 1990 criteria for the classification of polyarteritis nodosa. Arthritis Rheum 1990; 33(8): 1088-93. 18. Arend WP, Michel BA, Bloch DA et al. The American College of Rheumatology 1990 criteria for the classification of Takayasu arteritis. Arthritis Rheum 1990; 33(8): 1129-34. 19. Callen JP. Cutaneous vasculitis: what have we learned in the past 20 years? Arch Dermatol 1998;134(3):355-7 20. Jennette JC, Falk RJ. Do vasculitis categorization systems really matter? Curr Rheumatol Rep 2000; 2(5): 430-8 21. Sunderkotter C, Sindritaru A. Clinical classification of vasculitis. Eur J Dermatol 2006; 16(2):114-24. 22. Watts RA, Scott DG. Classification and epidemiology of the vasculitides. Baillieres Clin Rheumatol 1997; 11 (2): 191-217 Table I. Causes of vasculitis in our study (n=62) Causes Number (%) Histomorphology Primary 48 (77.4) Neutrophilic (n=22) Lymphocytic (n=13) Eosinophilic (n=10) Granulomatous (n=3) Secondary 14 (22.5) Drugs 7 (50) Neutrophilic (n=3) Lymphocytic (n=3) Eosinophilic (n=1) Infections 3 (21.4) Neutrophilic (n=2) Granulomatous (n=1) Connective tissue disorders 3 (21.4) Lymphocytic (n=3) Malignancy 1 (7.1) Neutrophilic (n=1) Table II: Clinical features of cases with histologically proven vasculitis Clinical feature Number (%) Arthralgia/ arthritis 45 (72.5) Palpable purpura 34 (54.8) Maculopapular rash 18 (29.0) Fever 15 (24.1) Urticaria 12 (19.3) Nodule 4 (6.4) Papule 4 (6.4) Ulcer 2 (3.2) Haematuria 1 (1.6) Table III: Laboratory parameters of patients of patients with histologically proven vasculitis Parameter Positive Negative Not done Anemia 12 30 20 Raised ESR 50 12 Leukocytosis with neutrophilia 11 31 20 Eosinophilia 4 38 20 Thrombocytopenia 8 42 12 Kidney function tests 4 58 ANA 12 22 28 Anti-ds DNA 6 28 28 ANCA 16 46 CRP 12 26 24 Anti HCV 5 57 ASO titre 8 15 39 Table IV: Association of histomorphological diagnosis with clinical impression HISTOPATHOLOGICAL DIAGNOSIS CLINICAL DIAGNOSIS Primary Small Vessel Vasculitis (n=48) Neutrophilic / Leukocytoclastic (n=22) Vasculitis(n=8) Pustular dermatosis (n=5) Microscopic polyangiitis (n=4) Rheumatoid vasculitis (n=2) Hypersensitivity vasculitis (n=1) Erythema Elevatun Diutinum (n=1) Henoch-Schonlein Purpura (n=1) Lymphocytic (n=13) Chronic Urticaria (n=4) Perniosis (n=3) Pityriasis Lichenoides (n=2) Atrophie Blanche (n=2) Erythema Annulare Centrifugum (n=1) Polymorphous Light Eruptions (n=1) Eosinophilic (n=10) Urticarial vasculitis (n=6) Prurigo nodularis (n=2) Hypersensitivity vasculitis (n=1) Granuloma faciale (n=1) Granulomatous (n=3) Allergic granulomatosis (n=2) Churg-Strauss Syndrome (n=1) Secondary Small Vessel Vasculitis (n= 12) Neutrophilic (n=6) Drug reaction (n=3) Behcet’s disease (n=1) Sweets syndrome (n=1) Acute neutrophilic dermatosis (n=1) Lymphocytic (n=5) Drug reaction (n=4) Discoid lupus erythematosis (n=1) Granulomatous (n=1) Wegener’s granulomatosis (n=1) Medium vessel vasculitis (n=2) Neutrophilic (n=2) Polyarteritis Nodosa (n=2) LEGENDS Figure I: Palpable purpura Figure II: H E (20x10X): Neutrophilic vasculitis Figure III: HE (10x10X): Lymphocytic vasculitis Figure IV: HE (20x 10X): Granulomatous vasculitis Figure V: HE (20x10X): Medium vessel vasculitis with panniculitis

Huckleberry Finn and The Modern Classroom :: essays papers

Huckleberry Finn and The Modern Classroom Mark Twain’s story The Adventures of Huckleberry Finn, is a racist, immoral book that should not be taught in American High Schools. As a children’s story, Finn is an exciting tale of a boy and a runaway slave riding a raft to freedom. As a book to be taught to 16-year-old English students, it is a novel that incorporates serious racist issues conveniently hidden among it’s many scattered plots. From the beginning we are warned â€Å"persons attempting to find a plot will be shot,†(Notice) suggesting that, as analyzing novels is a central theme in English classrooms, Finn may not be the best choice. The protagonist, Huckleberry Finn, is a 14-year-old white boy growing up in Missouri, who lives his life running away from his problems, lying to everyone, stealing, and using everyone he comes across. He fakes his own death very convincingly, and all with the cool, level-headedness not akin to young boys. â€Å"Well, next I took an old sack and put a lot of big rocks in it, -all I could drag,-and I started it from the pig and dragged it to the door and through the woods down to the river and dumped it in, and down it sunk, out of sight. You could easy see that something had been dragged over the ground. I did wish Tom Sawyer was there, I knowed he would take an interest in this kind of business, and throw in the fancy touches. Nobody could spread himself like Tom Sawyer in such a thing as that.†(Ch. 7) This character isn’t probably what the youth of America needs to be learning about. His ability to remorselessly lie to people to get what he wants, is a frightening characteristic, which isn’t one that should be taught to impressionable students. He has taken the pretense of his own death and, in his mine, placed it in the category of the imaginary robbers and thieves games he used to play with Tom Sawyer. Huck’s companion on his trip down the river is Jim, an uneducated adult, black slave who has run away hoping to make it to a free state. The way that Huck treats Jim at times, lying to him or belittling him is racist and wrong.

Diamonds in the Rough :: Nature Rocks Outdoors Essays

Diamonds in the Rough Nature is full of many awe-inspiring things, from majestic mountains to carpets of flowers. There is much artistic creativity inspired by nature, but it is often of valleys, and streams. Rarely do we see the smaller pieces that make up such grand pictures. There are few people who appreciate the beauty of a single leaf, or a single drop of water. It is even rarer to find a person who finds beauty in a rock. For most people rocks are only beautiful if polished up and set in gold or silver. I am certainly no exception, however, I am often intrigued by the lower class of rocks. It takes a child, or an adult in touch with their inner child, to find the potential of the average, dirt covered rock. Through the eyes of a child, each rock takes on a personality, be it a country cousin or a snooty countess. Come through the eyes of a child and experience the beauty and majesty of a rock, from the simple stone to the classy diamond. On our daily journeys we often pass by the humblest of rocks, those that decorate our gardens, or the ones that are simply buried in the dirt at the park. Most of us see a rock, if we see it all. These quiet stones are the lowest caste of the rock world, but they do not lack their own impressiveness. They come in all shapes and sizes, from large and smooth, to small with jagged edges. They even come in different colors and patterns, swirled greys, and pale creams, deep browns, and smooth reds. Like fingerprints, or people themselves, no rock is like any other. These rocks are a chid’s friend, another door to the imagination. Children use them to build houses for gnomes, and pretend they are people. We adults simply smile and indulge the child, never once looking beyond the rock. Yet sometimes I find myself imagining this plain grey rock’s journey. Did it form in the bowels of the earth, from molten rock? Did it work its way to the surface over cen turies of time?

Asahi Glass

TABLE OF CONTENTS Executive Summary3 Recognizing Opportunities4 Company Structure 5 Issues Facing Asahi Glass5 Questions to Answer6 Conclusion6 Recommendations6 Executive Summary Asahi Glass Company was founded in the early 1900’s to relieve Japan’s dependence on foreign imports. It was the first successful endeavor into the flat glass industry. The company was able to continue to succeed through mergers, acquisitions, and organic growth. The company’s core businesses are: 1. Glass and related products, 2. Chemical products, 3. Ceramics and refractory products, 4. Electronic products, and 5. All other miscellaneous products The synergies that were created by combining management’s expertise with the company’s knowledge, resources, and technologies have contributed to the success of Asahi Glass Company throughout the years. The organizational structure of Asahi Glass’ domestic productions are effective for their business’. There is a top down management system, with each division having its own managers and balance sheet. However, globalization efforts have been depleting company resources in past years. Management has yet to be able to perfect their foreign operating organization. The company is unable to establish mutual trusting relationship with several overseas joint ventures. ? Asahi Glass Company was founded in 1907, by Toshiya Iwasaki, a nephew of one of the founders of the Mitsubishi business group. Iwasaki wanted to ease Japan’s dependence on imports, by establishing a flat glass industry. It took three years after production started in 1909 to make a profit, but the endeavor was well worth it; Asahi Glass Company established themselves as the dominate player in the market and has remained that way ever since. Throughout Asahi Glass’ existence, their decisions and objectives have been focused on growth. They achieve this by exploring new technologies and growing organically, as well as acquiring companies, and merging with others. Their management style is also a key factor to their success. Recognizing Opportunities During the First World War, Asahi Glass was having trouble importing the soda ash they needed for manufacturing, so they started producing it themselves. This led the company into the exploitation of the raw-materials scope economies. They soon developed technological expertise in ceramics and alkali chemicals, which became two of the three core business â€Å"pillars. † After World War II, management made a sensible strategic decision to license a new float glass process from the Pilkington Brothers in order to maintain their market position. In the 1960s, Asahi Glass took advantage of growing TV and auto industries, and moved into them, becoming a domestic leader in both industries. Soon after, they progressed into producing construction materials. When the chemical industry took off in Japan, Asahi Glass merged in with their alkalis, halogen, and other petrochemical additives. They were market leaders in every industry they infiltrated. Asahi Glass created new, unique markets and took the lead in many specialty markets. In the 1970s, the current president, Takeo Sakabe, took the initiative to introduce a fourth â€Å"pillar† to the company’s core businesses: electronics. He chose electronics because management had some expertise in it and the industry had room for growth. Asahi Glass began penetrating the global market in 1956, when they built a plant in India. Then, the company entered into joint ventures in Thailand and Indonesia in 1964 and 1972 respectively. Not long after establishing their presence in those markets for glass, Asahi Glass’ chemical business followed into the areas. Once the company began to expand, they accelerated their efforts through the 1990s. Company Structure Asahi Glass had â€Å"a matrix style organization structure. † Each of the six general divisions and the five individual divisions had their own managers and kept their own balance sheet. Asahi Glass had an International General Division, which communicated with domestic product divisions, and monitored the subsidiaries and affiliates who were abroad, as well as help formulate business plans. The company tried to localize their oversea activities, and let them manage day-to-day operations and only held executive meetings about once every four months. Issues Facing Asahi Glass In 1993, Asahi Glass’s domestic glass business was declining due to the Japanese economy. The answer for the company was to continue globalization efforts. However, the company’s quick response and accelerated efforts caused the company to lose focus of their traditional international practices. The company’s domestic operational structure was not the same as their international operating structure. Because many of the international were joint ventures, and still relatively fresh, the two companies still lacked trust and coordination. Asahi Glass was still realizing that moving into foreign markets took more integration and stronger efforts than operating domestically. Questions to Answer In 1993, president Seya was faced with a decision for the electronics department. He was analyzing a report of long term strategy for the business, and the position of its major products. The report offered proposals ranging from intense divesting, to rigorous investing. Mr. Seya needed to decide if investing the capital needed to ascertain a dominant position in the electronics business was worth the risk. His decision would be the foundation of the business’ strategic direction and he felt that direction should be aligned with Asahi Glass’ other divisions, and their overall objectives for the years to come. Conclusion Asahi Glass Company has always been an aggressive, dominant company. They exhort their knowledge, expertise, and technology in order to gain a leading position in whatever industry or market they endeavor. In the latest years, it seems that the company is trying to spread themselves too thin by globalizing. Until Asahi Glass finds a better way to organize and operate their foreign affairs, they should focus on domestic mergers, acquisitions, and internal growth. Recommendations I believe that Asahi Glass has had an excellent history of creating successful synergies that have propelled the company to success. From its beginnings in the early 1900s, the company’s management has recognized opportunities to expand their core businesses and grow organically. As their core businesses expanded, so did the company’s knowledge, experience, and technology. As these assets have interacted over the years, they have combined to make synergies that allowed the company to expand into new markets, products, and industries. Asahi Glass has an excellent foundation in the way of management as well as financial prosperity. I recommend that Asahi Glass invest in the establishment of the electronic business as a dominant position. Looking at the electronics business’ history shows that the division is among the top three in relative market position already, despite that they have a low share in the market (exhibit 10). They are also already well established, having joint ventures with at least five companies, three of which are in the top six market positions (exhibit 10). The electronic division contributed 5. 6% to sales in 1992; compared to ceramics contribution of 2. 4% (exhibit 6).

Paradise of the Blind

World literature two Statement of intent I plan to elaborate on the political aspects of Paradise of the Blind by writing a formal letter to the Vietnam government. The Vietnam government banned this book from the country because of the all encompassing political aspects included in the book. In the authors books all of the aspects of life are demonstrated and the total view was an unattractive image for the leaders. This book was banned because it went against what a government hopes to portray to their people.Duong Thu Huong worked for the Communist Youth Brigade at the age of 20 but was expelled from the communist party in 1989. She has been imprisoned numerous times for her outspoken support for human rights and democratic political reform and is no longer allowed to leave Vietnam. In Paradise of the blind, her fourth book written and her fourth book to be banned, she included government acts such as the effects of communists on the people such as the land reform act, and Ratific ation of Errors. The large political attraction and its effect on the people are a large part of her books but the cultural aspects are also prominent.I hope to convince the Vietnamese government to publish this book which would provide a better understanding for the people in and out of Vietnam. The reason I chose to do a letter it because it allows me to show the government’s large and influential part in the people’s lives and as well as discus the cultural aspects. The government banned the book in 1991 and I wanted to explore why it was important enough to ban and hopefully have an objective view on why it would be good to publish it. President Truong T? n Sang,Thank you for the honor of taking time out of your day to read my letter. I would like to address the Book Paradise of the Blind by Duong Thu Huong. Because it was banned from your country you might not be familiar with the work but I am sure you are aware of the author. She is well known for her writing an d political stances. Her book is a fiction with many real life situations and probable events for a person of Vietnam in the 1980’s. Because of this factor I found it interesting that it was banned from your country under the term of President (Insert name here).I write to you today in hopes that you will reevaluate president (insert name here)’s decision and allow this book to be published. The social conditions were not optimal in the book and I can respect the decision to ban it, but it was banned from the country but that was a different time and things have changed since then. Under the influence of many years the conditions and circumstances have changed. Publishing the book provides you with a good opportunity to portray an understanding for the people and your willingness to cooperate with their wants with little or no cost to you.Duong Thu Huong has spoken out about the government censorship numerous times and by allowing her book to be published now, it could allow the people to trust their government. The book showed a vulnerable time in Vietnamese history fraught with trials and errors on the part of the government. The fact that it revealed corruption in the government was a problem at the time but the government has improved since then. After the Ratification of Errors the land owners were allowed to prosper again such as â€Å"Aunt Tam† even though times were still difficult.Like the Ratification of errors after the land reform, by ending the ban it would show that you as a country are willing to accept the past and move on. As the governmental issues discussed in the book are not as prominent today it would be beneficial for the government’s reputation to show how far they have come. As I read the book I soon realized what insight it would provide an understanding into the lifestyle of the older generations. It would allow the students of today to better understand their countries history and how it affected the peop le’s lives.The insight that the book provided Americans into Vietnam could also be mirrored by the Vietnamese and a new appreciation for their culture could arise. By allowing the people to see the conditions back then they will realize the full extent of the improvements made and understand their parents and grandparents went though. The food in the book allows for a better understanding of the situation. At each time in the characters life, depending on their financial situation, the food varied. The fact that there is an emphasis on food would be understood and relatable to the public.They have the ability to compare condition then and now creating a bond that comes from true understanding. Thought this book good traits, situations, and food are juxtaposed with bad and the influence that they had on their surroundings is something that can be learned from. This book demonstrates a strong aspect of your culture in the submission of Que to her brother. When her brother told her to leave her husband she did even though she loved him. Her devotion to her brother demonstrates the characteristics of a good sister and later aunt.Que went to the tenement were her brother lived in order to care for her brother and his family. She put her brother’s needs in front of her own and her daughters. When her job was not acceptable in her brother’s opinions she reluctantly changed jobs. That selflessness in her endeavor to help her brother is something to be admired. Just as Que helped her brothers family, Hang was loyal to her mother. When her mother had her leg amputated due to a car accident and could no longer be an efficient worker, Hang hurried to help her. Hang quit school to go to Russia to work and support her mother.Tam also had admirable qualities in that she supported her brother’s child when Hang’s mother had practically abandoned her for her nephews. Tam supported Hang through school and gave her many opportunities to succeed. The traits in all three of these women are admirable and the general public could learn from their sacrifices. The hard work that they put in gave others the ability to live and do as they needed to survive. Paradise of the Blind is an interesting book that taught me a lot about your culture. It advocates many good qualities as well as change.Hang’s decision to progress into the future and leave her past behind when she decided to sell her Aunt’s house allows her to move on and create a new opportunity to better herself. By releasing the book the same opportunity would come for you and learning form the past would further strengthen yourself today. People could compare what is to what was and make up their opinion for themselves. The people will appreciate the freedom and respect you for allowing them the option. By leaving the book banned you lead people to believe that it is still true today when much change has occurred.