The River Rouge Manufacturing Complex. Essays -

The River Rouge Manufacturing Complex. The first piece of material I gathered was a picture via the internet. This picture is of the River Rouge assembly plant in Dearborn, Michigan. This picture shows the manufacturing of the fender for a Ford Motor Company product. It also shows the facilities of the Rouge plant and how the plant it self was state of the art. This plant was the largest of its kind at the time of its construction. The Ford Motor Company at the time was one of the leaders in labor relations. This picture shows the size of the plant as well as the working conditions in the facility. When viewing the photograph you can see the array of pipes and collection devices to aid in the circulation of air and the collection of dust and other by products made in the plant. The next component I found is another picture of the interior of the Rouge plant. This picture is one of many conveyer belts in the plant. This belt is moving engine parts from the engine assembly to the final assembly. Henry Ford was a pioneer in the use of the assembly line in the automobile industry, and the Rouge plant was the ultimate in that use of the assembly line. This photo shows the depth of the plant, being able to manufacture all components of the cars without having to ship parts to or from other locations in the country. The next collection of photographs is of the exterior of the Rouge plant. These photos were obtained from the Henry Ford Museum in Dearborn, Michigan. These pictures are of the Rouge during the switch of all production, from the Highland Park plant, to the Rouge. It was also the time that the Model A was beginning production. This collection shows examples of four exterior views of the plant, allude to the many different factories within the Rouge plant. The Rouge was a steel mill, a foundry, a power producer and, an assembly line. This all encompassing idea helped ford relegate all aspects of the production of their product. Along with the exterior, the interior showed the extent of the all encompassing Rouge plant. The interior photographs, which were also care of the Henry Ford Museum, show more factories within the factory. For example, the four photos in this collection display metal forming, and metallurgical operations. These pictures included forging, the blast furnaces, removal of slag and, even salvaging scrap from metal ships. The interior had two collections to view and the second reaffirmed what the first portrayed. The second collection displays more metal working production including the hydraulic shear, which was used for sheet metal, the open hearth ladle and the hearth building. These photos gave an impressive direction of the inner workings of the Rouge plant. As said before the Rouge was the largest manufacturing complex in the nation when it was built. An aerial photograph of the plant reaffirms that fact. The photo was taken in 1930 and you can see by the photo the plant is very impressive. The caption that accompanies the picture gives an actual figure of the Rouge's square footage, the total is 6,952,484 square feet. Before the Rouge plant Ford's main manufacturing plant was Highland Park. The Rouge and Highland Park were similar in the way of utilizing the assembly line to produce the Ford product. Many collections of photos were found of the assembly line at Highland. One collection shows the final mating of the model T, which is similar to the final mating of the model A . Also the one day production of the Highland Park plant, which was dwarfed by the Rouge one day production total. The next collection of Highland Park photos displays the typical procedures in installing components to the automobile. Each of the four pictures shows the installation to the car. From the engine to the tires the same principles that were used at Highland Park were used at the Rouge plant. The final piece of material that was compiled through the search of the Internet and other sources was the National Historic Landmark of Michigan web page. This page has a link to an informational page on the Rouge plant. The plant is listed as a national landmark since 1978 and a Michigan landmark since 1976. Also listed on the site is the date the property was bought by Henry Ford and, the date all production was shifted from Highland Park to the Rouge complex. A significant statement is given about the Rouge on

Green Anaconda essays

Green Anaconda essays The Green Anaconda is considered to be the biggest snake in the world but this is not the longest. Physically, it is dark green with black elliptical patches and the length is 9 meters. It can weigh a hefty 250 kg and have a girth of more than 12 inches in diameter (Szdocent, 2006). Anaconda can eat an elephant because their jaw can expand. Scientific Classification taken from Busch Entertainment Corporation (2006): Common Name: green anaconda, common anaconda, water boa Genus Species: Eunectes (good swimmer) murinus (mouse colored) Green Anacondas live in water most of the time; they are good swimmers and can travel in fast motion. But they cannot move fast in the land since they are big enough to carry themselves. The head of the anaconda usually lies on the surface of the water while the body is underwater but they can submerge the whole body for a maximum of 10 minutes to catch big fish to nourish themselves and in the surface they catch birds that at the same time try to capture a fish. Anaconda eats anything as long as it is edible for them; they are also believed to be cannibalistic since they eat their own species. People are not on the menu, but caimans (similar to alligators), capybaras (100-pound South American rodents) and deer are (Shedd, 2006). They have very sharp teeth and always lying without moving until they see a victim that they move quickly, grab them and bring them under water to eat. According to Szdocent (2006): Anacondas are constrictors. The snake squeezes tighter each time its prey breathes out, so the prey cannot breathe in again. This goes on until the prey dies of suffocation. Anacondas swallow their prey whole, starting with the head. This is so the legs fold up and the prey goes down smoothly. Anacondas can swallow prey much bigger than the size of its mouth since its jaw can unhinge and the ...

Drunk Driving Essay Example | Topics and Well Written Essays - 500 words

Drunk Driving - Essay Example Research has also shown that an alcohol related death occurs in every thirty minutes and driving while drunk accounts for approximately 25% of the deaths. Drinking has been found to impair vision and hearing among drinkers, it also leads to a decline in terms of coordination of the body muscles and ultimately reaction times. This is a combination that may not be desired for people expected to drive on our roads. At a BAC of above ten percent for instance one to have a very poor reaction time, deteriorated motor control, poor vision, mood changes and over expression. There is no doubt such is not the status expected drive a car. As a result of the sensory impacts of drinking alcohol, driving while is responsible for the many accidents and deaths associated with alcohol. it would therefore be advisable that all drivers avoid drinking if they have to drive or wait until the end of the journey if they must drink. Laws have been put in place to punish those found to drive while drunk and therefore driving while drunk means violating state laws. DUI is a crime and may be classified as either a felony or misdemeanor depending on the circumstances in which the crime is committed. A misdemeanor is preferred when the drunk driver causes no injury to other persons or peoples’ properties. A felony on the other hand results when DUI results in injuries. The maximum jail term in case of a misdemeanor is six months whereas for felony it can extend unto three years. Punishment may also include revocation of one’s driving license. Research has also shown that peer pressure is a major case DUI; the youth would want to drink and drive in a quest to show off to peers, pretend to be strong, impress their friends or even just because they don’t want others to drive their car. This has the effect of exposing the teenagers and youth at a much higher risk of engaging in DUI and by extension related accidents as compared to the adults

(Annotated Bibligraphy) Finding 10 acadamic sources and writing a Essay

(Annotated Bibligraphy) Finding 10 acadamic sources and writing a brief description of those 6 entries(at least 3 sentences) - Essay Example The wars took place in a furious fight to control territory and resources. As violence escalate, laws were legislated to support the war policy of nations, e.g. espionage, albeit complaints of people who uphold freedom of speech against the monstrosity of forced servitude for war and to reassert their constitutional human rights. Author is from Brooklyn who was directly involved in WWII which motivated him to advocate for peace agenda later in his life. In 1956, he became a professor at Spellman College in Atlanta, a school for black women at the height of Civil rights movement. He was an adviser of to the Student Nonviolent Coordinating Committee (SNCC). This chapter of the book explicated the racial discrimination and the trafficking of black men who were subjected to severe slavery in North America. Many of them were forced to partake labor for agriculture—to grow rice, corn, and tobacco. Author is directly involved in WWII which motivated him to advocate for peace agenda. In 1956, he became a professor at Spellman College in Atlanta, a school for black women at the height of Civil rights movement. He was an adviser of to the Student Nonviolent Coordinating Committee (SNCC). This is a book which relates about post 9/11 incident and the consequential bombings done in Afghanistan as retaliatory moves against suspected terrorists. The author reflected about his experience as ‘bombardier’ in the Second World War and concluded that â€Å"any war, no matter how good, cannot solve the problems.† The author, as a warrior, has experiential stories of his participation on wars in Japan, Korea, Vietnam, Panama, Iraq and Yugoslavia. In this story, author reflected that while there are so much theoretical concepts about democracy, freedom, national security, there remains a large majority who are poor, hungry and sick. Author advocated for the reversal of the roles of heroes and villains following his deep involvement in

Design for change in practice Research Paper Example | Topics and Well Written Essays - 1250 words

Design for change in practice - Research Paper Example Design for change in practice is largely based on the framework proposed by Rossuwurm and Larrabee (1999). According to Rossuwurm and Larrabee (1999), design for change in practice is manifested through protocols, procedures, or standards. In the case with communicating terminal diagnosis to patients in order to improve their quality of life design relies on the mechanism of protocols distributed to medical personnel. Since the overall complexity of design determines the likelihood of change acceptance (Rossuwurm and Larrabee, 1999, p.320), protocols contain only well-structured and detailed information regarding terminal diagnosis disclosure practice and follow up procedures aimed to improve patients' quality of life. Because feedback from patients (stakeholders) is essential when designing a change in practice (Rossuwurm and Larrabee, 1999), design also includes a questionnaire produced to measure changes in patients' quality of life. (1) Practice of terminal diagnosis full disclosure. ... As explained by Fitch (1994) once a word like "cancer" or "terminal" is heard, a mental retreat is often enacted and thus true communication may not take place until the stark essence of the initial message has been absorbed. Similarly, when news of terminality are communicated rapidly, the patient may remember very little of what has been said. Therefore, in order to implement this change in practice some patients should have information about their terminal diagnosis divided into manageable stages so that self-perceptions gradually shift from well, to ill, to dying over a period of days or weeks. The fundamental premise of this change in practice is that physicians should exclusively opt the strategy of full disclosure of terminal diagnosis for their patients. (2) Eliminating avoidance practices. The issue of death and dying like any topic or situation that creates anxiety tends to trigger avoidance responses. In medical practice, two types of avoidance may be seen: physical avoidance, in which an individual makes an effort to avoid being in the presence of persons, places or objects that cause anxiety; and topical avoidance, in which a clinician avoids thinking or talking about an anxiety-producing issue. Physicians and nurses tend to avoid patient feelings (i.e., depression, anger, or anxiety) by focusing on the explicit content of the patients' message (Dilbeck, 1996). Related to this response in medical personnel is the finding that patients report that technological interventions and procedures cause them to feel that their need for support through compassion and caring is being denied (Super & Plutko, 1996). Patients cannot

Compression Techniques used for Medical Image

Compression Techniques used for Medical Image 1.1 Introduction Image compression is an important research issue over the last years. A several techniques and methods have been presented to achieve common goals to alter the representation of information of the image sufficiently well with less data size and high compression rates. These techniques can be classified into two categories, lossless and lossy compression techniques. Lossless techniques are applied when data are critical and loss of information is not acceptable such as Huffman encoding, Run Length Encoding (RLE), Lempel-Ziv-Welch coding (LZW) and Area coding. Hence, many medical images should be compressed by lossless techniques. On the other hand, Lossy compression techniques such as Predictive Coding (PC), Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT) and Vector Quantization (VQ) more efficient in terms of storage and transmission needs but there is no warranty that they can preserve the characteristics needed in medical image processing and diagnosis [1-2]. Data compression is the process that transform data files into smaller ones this process is effective for storage and transmission. It presents the information in a digital form as binary sequences which hold spatial and statistical redundancy. The relatively high cost of storage and transmission makes data compression worthy. Compression is considered necessary and essential key for creating image files with manageable and transmittable sizes [3]. The basic goal of image compression is to reduce the bit rate of an image to decrease the capacity of the channel or digital storage memory requirements; while maintaining the important information in the image [4]. The bit rate is measured in bits per pixel (bpp). Almost all methods of image compression are based on two fundamental principles: The first principle is to remove the redundancy or the duplication from the image. This approach is called redundancy reduction. The second principle is to remove parts or details of the image that will not be noticed by the user. This approach is called irrelevancy reduction. Image compression methods are based on either redundancy reduction or irrelevancy reduction separately while most compression methods exploit both. While in other methods they cannot be easily separated [2]. Several image compression techniques encode transformed image data instead of the original images [5]-[6]. In this thesis, an approach is developed to enhance the performance of Huffman compression coding a new hybrid lossless image compression technique that combines between lossless and lossy compression which named LPC-DWT-Huffman (LPCDH) technique is proposed to maximize compression so that threefold compression can be obtained. The image firstly passed through the LPC transformation. The waveform transformation is then applied to the LPC output. Finally, the wavelet coefficients are encoded by the Huffman coding. Compared with both Huffman, LPC- Huffman and DWT-Huffman (DH) techniques; our new model is as maximum compression ratio as that before. However, this is still needed for more work especially with the advancement of medical imaging systems offering high resolution and video recording. Medical images come in the front of diagnostic, treatment and fellow up of different diseases. Therefore, nowadays, many hospitals around the world are routinely using medical image processing a nd compression tools. 1.1.1 Motivations Most hospitals store medical image data in digital form using picture archiving and communication systems due to extensive digitization of data and increasing telemedicine use. However, the need for data storage capacity and transmission bandwidth continues to exceed the capability of available technologies. Medical image processing and compression have become an important tool for diagnosis and treatment of many diseases so we need a hybrid technique to compress medical image without any loss in image information which important for medical diagnosis. 1.1.2 Contributions Image compression plays a critical role in telemedicine. It is desired that either single images or sequences of images be transmitted over computer networks at large distances that they could be used in a multitude of purposes. The main contribution of the research is aim to compress medical image to be small size, reliable, improved and fast to facilitate medical diagnosis performed by many medical centers. 1.2 Thesis Organization The thesis is organized into six chapters, as following: Chapter 2 Describes the basic background on the image compression technique including lossless and lossy methods and describes the types of medical images Chapter 3 Provides a literature survey for medical image compression. Chapter 4 Describes LPC-DWT-Huffman (proposed methods) algorithm implementation. The objective is to achieve a reasonable compression ratio as well as better quality of reproduction of image with a low power consumption. Chapter 5 Provides simulation results of compression of several medical images and compare it with other methods using several metrics. Chapter 6 Provides some drawn conclusions about this work and some suggestions for the future work. Appendix A Provides Huffman example and comparison between the methods for the last years. Appendix B Provides the Matlab Codes Appendix C Provides various medical image compression using LPCDH. 1.3 Introduction Image compression is the process of obtaining a compact representation of an image while maintaining all the necessary information important for medical diagnosis. The target of the Image compression is to reduce the image size in bytes without effects on the quality of the image. The decrease in image size permits images to save memory space. The image compression methods are generally categorized into two central types: Lossless and Lossy methods. The major objective of each type is to rebuild the original image from the compressed one without affecting any of its numerical or physical values [7]. Lossless compression also called noiseless coding that the original image can perfectly recover each individual pixel value from the compressed (encoded) image but have low compression rate. Lossless compression methods are often based on redundancy reduction which uses statistical decomposition techniques to eliminate or remove the redundancy (duplication) in the original image. Lossless Image coding is also important in applications where no information loss is allowed during compression. Due to the cost, it is used only for a few applications with stringent requirements such as medical imaging [8-9]. In lossy compression techniques there are a slight loss of data but high compression ratio. The original and reconstructed images are not perfectly matched. However, practically near to each other, this difference is represented as a noise. Data loss may be unacceptable in many applications so that it must be lossless. In medical images compression that use lossless techniques do not give enough advantages in transmission and storage and the compression that use lossy techniques may lose critical data required for diagnosis [10]. This thesis presents a combination of lossy and lossless compression to get high compressed image without data loss. 1.4 Lossless Compression If the data have been lossless compressed, the original data can be exactly reconstructed from the compressed data. This is generally used for many applications that cannot allow any variations between the original and reconstructed data. The types of lossless compression can be analyzed in Figure 2.1. Figure 2.1: lossless compression Run Length Encoding Run length encoding, also called recurrence coding, is one of the simplest lossless data compression algorithms. It is based on the idea of encoding a consecutive occurrence of the same symbol. It is effective for data sets that are consist of long sequences of a single repeated character [50]. This is performed by replacing a series of repeated symbols with a count and the symbol. That is, RLE finds the number of repeated symbols in the input image and replaces them with two-byte code. The first byte for the number and the second one is for the symbol. For a simple illustrative example, the string AAAAAABBBBCCCCC is encoded as A6B4C5; that saves nine bytes (i.e. compression ratio =15/6=5/2). However in some cases there is no much consecutive repeation which reduces the compression ratio. An illustrative example, the original data 12000131415000000900, the RLE encodes it to 120313141506902 (i.e. compression ratio =20/15=4/3). Moreover if the data is random the RLE may fail to achieve any compression ratio [30]-[49]. Huffman encoding It is the most popular lossless compression technique for removing coding redundancy. The Huffman encoding starts with computing the probability of each symbol in the image. These symbols probabilities are sorted in a descending order creating leaf nodes of a tree. The Huffman code is designed by merging the lowest probable symbols producing a new probable, this process is continued until only two probabilities of two last symbols are left. The code tree is obtained and Huffman codes are formed from labelling the tree branch with 0 and 1 [9]. The Huffman codes for each symbol is obtained by reading the branch digits sequentially from the root node to the leaf. Huffman code procedure is based on the following three observations: 1) More frequently(higher probability) occurred symbols will have shorter code words than symbol that occur less frequently. 2) The two symbols that occur least frequently will have the same length code. 3) The Huffman codes are variable length code and prefix code. For more indication Huffman example is presented in details in Appendix (A-I). The entropy (H) describes the possible compression for the image in bit per pixel. It must be noted that, there arent any possible compression ratio smaller than entropy. The entropy of any image is calculated as the average information probability [12]. (2.1) Where Pk is the probability of symbols, k is the intensity value, and L is the number of intensity values used to present image. The average code length is given by the sum of product of probability of the symbol and number of bits used to encode it. More information can be founded in [13-14] and the Huffman code efficiency is calculated as (2.2) LZW coding LZW (Lempel- Ziv Welch) is given by J. Ziv and A. Lempel in 1977 [51].T. Welchs refinements to the algorithm were published in 1984 [52]. LZW compression replaces strings of characters with single codes. It does not do any analysis of the input text. But, it adds every new string of characters to a table of strings. Compression occurs when the output is a single code instead of a string of characters. LZW is a dictionary based coding which can be static or dynamic. In static coding, dictionary is fixed during the encoding and decoding processes. In dynamic coding, the dictionary is updated. LZW is widely used in computer industry and it is implemented as compress command on UNIX [30]. The output code of that the LZW algorithm can be any arbitrary length, but it must have more bits than a single character. The first 256 codes are by default assigned to the standard character set. The remaining codes are assigned to strings as the algorithm proceeds. There are three best-known applications of LZW: UNIX compress (file compression), GIF image compression, and V.42 bits (compression over Modems) [50]. Area coding Area coding is an enhanced form of RLE. This is more advance than the other lossless methods. The algorithms of area coding find rectangular regions with the same properties. These regions are coded into a specific form as an element with two points and a certain structure. This coding can be highly effective but it has the problem of a nonlinear method, which cannot be designed in hardware [9]. 1.5 Lossy Compression Lossy Compression techniques deliver greater compression percentages than lossless ones. But there are some loss of information, and the data cannot be reconstructed exactly. In some applications, exact reconstruction is not necessary. The lossy compression methods are given in Figure 2.2. In the following subsections, several Lossy compression techniques are reviewed: Figure 2.2: lossy compression Discrete Wavelet Transform (DWT) Wavelet analysis have been known as an efficient approach to representing data (signal or image). The Discrete Wavelet Transform (DWT) depends on filtering the image with high-pass filter and low-pass filter.in the first stage The image is filtered row by row (horizontal direction) with two filters and and down sampling (keep the even indexed column) every samples at the filter outputs. This produces two DWT coefficients each of size N ÃÆ'-N/2. In the second stage, the DWT coefficients of the filter are filtered column by column (vertical direction) with the same two filters and keep the even indexed row and subsampled to give two other sets of DWT coefficients of each size N/2ÃÆ'-N/2. The output is defined by approximation and detailed coefficients as shown in Figure 2.3. Figure 2.3: filter stage in 2D DWT [15]. LL coefficients: low-pass in the horizontal direction and lowpass in the vertical direction. HL coefficients: high-pass in the horizontal direction and lowpass in the vertical direction, thus follow horizontal edges more than vertical edges. LH coefficients: high-pass in the vertical direction and low-pass in the horizontal direction, thus follow vertical edges than horizontal edges. HH coefficients: high-pass in the horizontal direction and high-pass in the vertical direction, thus preserve diagonal edges. Figure 2.4 show the LL, HL, LH, and HH when one level wavelet is applied to brain image. It is noticed that The LL contains, furthermore all information about the image while the size is quarter of original image size if we disregard the HL, LH, and HH three detailed coefficients shows horizontal, vertical and diagonal details. The Compression ratio increases when the number of wavelet coefficients that are equal zeroes increase. This implies that one level wavelet can provide compression ratio of four [16]. Figure 2.4: Wavelet Decomposition applied on a brain image. The Discrete Wavelet Transform (DWT) of a sequence consists of two series expansions, one is to the approximation and the other to the details of the sequence. The formal definition of DWT of an N-point sequence x [n], 0 à ¢Ã¢â‚¬ °Ã‚ ¤ n à ¢Ã¢â‚¬ °Ã‚ ¤ N à ¢Ã‹â€ Ã¢â‚¬â„¢ 1 is given by [17]: (2.3) (2.4) (2.5) Where Q (n1 ,n2) is approximated signal, E(n1 ,n2) is an image, WQ (j,k1,k2) is the approximation DWT and W µ (j,k1,k2) is the detailed DWT where i represent the direction index (vertical V, horizontal H, diagonal D) [18]. To reconstruct back the original image from the LL (cA), HL (cD(h)), LH (cD(v)), and HH (cD(d)) coefficients, the inverse 2D DWT (IDWT) is applied as shown in Figure 2.5. Figure 2.5: one level inverse 2D-DWT [19]. The equation of IDWT that reconstruct the image E () is given by [18]: (2.6) DWT has different families such as Haar and Daupachies (db) the compression ratio can vary from wavelet type to another depending which one can represented the signal in fewer number coefficients. Predictive Coding (PC) The main component of the predictive coding method is the Predictor which exists in both encoder and decoder. The encoder computes the predicted value for a pixel, denote xˆ (n), based on the known pixel values of its neighboring pixels. The residual error, which is the difference value between the actual value of the current pixel x (n) and x ˆ (n) the predicted one. This is computed for all pixels. The residual errors are then encoded by any encoding scheme to generate a compressed data stream [21]. The residual errors must be small to achieve high compression ratio. e (n) = x (n) xˆ (n)(2.7) e (n) =x(n) (2.8) Where k is the pixel order and ÃŽÂ ± is a value between 0 and 1 [20]. The decoder also computes the predicted value of the current pixel xˆ  (n) based on the previously decoded color values of neighboring pixels using the same method as the encoder. The decoder decodes the residual error for the current pixel and performs the inverse operation to restore the value of the current pixel [21]. x (n) = e (n) + xˆ  (n)(2.9) Linear predictive coding (LPC) The techniques of linear prediction have been applied with great success in many problems of speech processing. The success in processing speech signals suggests that similar techniques might be useful in modelling and coding of 2-D image signals. Due to the extensive computation required for its implementation in two dimensions, only the simplest forms of linear prediction have received much attention in image coding [22]. The schemes of one dimensional predictors make predictions based only on the value of the previous pixel on the current line as shown in equation. Z = X D(2.10) Where Z denotes as output of predictor and X is the current pixel and D is the adjacent pixel. The two dimensional prediction scheme based on the values of previous pixels in a left-to-right, top-to-bottom scan of an image. In Figure 2.6 X denotes the current pixel and A, B, C and D are the adjacent pixels. If the current pixel is the top leftmost one, then there is no prediction since there are no adjacent pixels and no prior information for prediction [21]. Figure 2.6: Neighbor pixels for predicting Z = x (B + D)(2.11) Then, the residual error (E), which is the difference between the actual value of the current pixel (X) and the predicted one (Z) is given by the following equation. E = X Z(2.12) Discrete Cosine Transform (DCT) The Discrete Cosine Transform (DCT) was first proposed by N. Ahmed [57]. It has been more and more important in recent years [55]. The DCT is similar to the discrete Fourier transform that transforms a signal or image from the spatial domain to the frequency domain as shown in Figure 2.7. Figure 2.7: Image transformation from the spatial domain to the frequency domain [55]. DCT represents a finite series of data points as a sum of harmonics cosine functions. DCTs representation have been used for numerous data processing applications, such as lossy coding of audio signal and images. It has been found that small number of DCT coefficients are capable of representing large sequence of raw data. This transform has been widely used in signal processing of image data, especially in coding for compression for its near-optimal performance. The discrete cosine transform helps to separate the image into spectral sub-bands of differing importance with respect to the images visual quality [55]. The use of cosine is much more efficient than sine functions in image compression since this cosine function is capable of representing edges and boundary. As described below, fewer coefficients are needed to approximate and represent a typical signal. The Two-dimensional DCT is useful in the analysis of two-dimensional (2D) signals such as images. We say that the 2D DCT is separable in the two dimensions. It is computed in a simple way: The 1D DCT is applied to each row of an image, s, and then to each column of the result. Thus, the transform of the image s(x, y) is given by [55], (2.13) where. (n x m) is the size of the block that the DCT is applied on. Equation (2.3) calculates one entry (u, v) of the transformed image from the pixel values of the original image matrix [55]. Where u and v are the sample in the frequency domain. DCT is widely used especially for image compression for encoding and decoding, at encoding process image divided into N x N blocks after that DCT performed to each block. In practice JPEG compression uses DCT with a block of 88. Quantization applied to DCT coefficient to compress the blocks so selecting any quantization method effect on compression value. Compressed blocks are saved in a storage memory with significantly space reduction. In decoding process, compressed blocks are loaded which de-quantized with reverse the quantization process. Inverse DCT was applied on each block and merging blocks into an image which is similar to original one [56]. Vector Quantization Vector Quantization (VQ) is a lossy compression method. It uses a codebook containing pixel patterns with corresponding indexes on each of them. The main idea of VQ is to represent arrays of pixels by an index in the codebook. In this way, compression is achieved because the size of the index is usually a small fraction of that of the block of pixels. The image is subdivided into blocks, typically of a fixed size of nÃÆ'-n pixels. For each block, the nearest codebook entry under the distance metric is found and the ordinal number of the entry is transmitted. On reconstruction, the same codebook is used and a simple look-up operation is performed to produce the reconstructed image [53]. The main advantages of VQ are the simplicity of its idea and the possible efficient implementation of the decoder. Moreover, VQ is theoretically an efficient method for image compression, and superior performance will be gained for large vectors. However, in order to use large vectors, VQ becomes complex and requires many computational resources (e.g. memory, computations per pixel) in order to efficiently construct and search a codebook. More research on reducing this complexity has to be done in order to make VQ a practical image compression method with superior quality [50]. Learning Vector Quantization is a supervised learning algorithm which can be used to modify the codebook if a set of labeled training data is available [13]. For an input vector x, let the nearest code-word index be i and let j be the class label for the input vector. The learning-rate parameter is initialized to 0.1 and then decreases monotonically with each iteration. After a suitable number of iterations, the codebook typically converges and the training is terminated. The main drawback of the conventional VQ coding is the computational load needed during the encoding stage as an exhaustive search is required through the entire codebook for each input vector. An alternative approach is to cascade a number of encoders in a hierarchical manner that trades off accuracy and speed of encoding [14], [54]. 1.6 Medical Image Types Medical imaging techniques allow doctors and researchers to view activities or problems within the human body, without invasive neurosurgery. There are a number of accepted and safe imaging techniques such as X-rays, Magnetic resonance imaging (MRI), Computed tomography (CT), Positron Emission Tomography (PET) and Electroencephalography (EEG) [23-24]. 1.7 Conclusion In this chapter many compression techniques used for medical image have discussed. There are several types of medical images such as X-rays, Magnetic resonance imaging (MRI), Computed tomography (CT), Positron Emission Tomography (PET) and Electroencephalography (EEG). Image compression has two categories lossy and lossless compression. Lossless compression such as Run Length Encoding, Huffman Encoding Lempel-Ziv-Welch, and Area Coding. Lossy compression such as Predictive Coding (PC), Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT), and Vector Quantization (VQ).Several compression techniques already present a better techniques which are faster, more accurate, more memory efficient and simpler to use. These methods will be discussed in the next chapter.

Advertising, Food and Children :: Expository Exemplification Essays

Advertising, Food and Children "Peanut, peanut butter and jelly." Advertising has an impact on its audience. From songs to logos to characters, food product advertisers must keep in mind their audiences. Competition is the force which causes advertisers to target children. Children are targeted through the use of cute phrases, animated characters, and toys in these competitive advertisements. Many types of food have a phrase associated with them. Commercials use these phrases to implant their product into the audiences memory. Goldfish crackers are an example of one these products. "I love the fishes `cause their so delicious..." This is the theme to a well known commercial which advertises Pepperigde Farm goldfish crackers. Children sing the phrase over and over throughout the entirety of the commercial. By the time the commercial ends the line and product are inevitably stuck in a persons mind. The commercial says "... and my mom says that's okay", which implies to children that their parents will allow them to eat this snack. Another example of a product with an addicting phrase is Oscar Meyer bologna. "My bologna has a first name its O-S-C-A-R..." Instead of this song selling the product itself, its aim is to sell the brand. The Oscar Meyer company has had auditions for the next Oscar Meyer child. Again, their goal is to sell their brand. The company also has another product with a catchy song, Oscar Meyer hot dogs. "I wish I were an Oscar Meyer wiener..." The stress of this phrase is also the brand. Oscar Meyer commercials use children to sing these songs and like the goldfish commercial, the song has been imprinted into a persons memory be the end of the commercial. Both companies goal is to sell their product. By targeting children, whole families are then targeted. Competition between companies with similar products, is the reason phrases are used. If one company can create a phrase that everyone will know and remember, they are one step closer to winning the race. Animated characters are also a medium used to target children. Animation has been the way which companies from Disney to Cartoon Network, capture the attention of children everywhere. Tony the Tiger is the spokesperson for Kellogg's frosted flakes.