Image Super-Resolver using Cascaded Linear Regression

Image Super-Resolver using Cascaded Linear Regression Abstract A number of existing super-resolution algorithms fail in modeling the relationship between high and low resolution image patches and time complexity in training the model. To overcome the above-stated problem, simple, effective, robust and fast image super-resolver (SERF) based on cascaded linear regression has been used for learning the model parameters. The image divided into patches are grouped into clusters using k-means clustering algorithm for learning the model parameter based on series of linear least square function, named cascaded linear regression to identify the missing detail information. This approach has been simulated using MATLAB for various images. The simulation results show that SERF gives better PSNR and less computation cost compared to existing methods. Keywords-Cascaded linear regression, example learning based image super-resolution, K-means. Super-Resolution (SR) is the process of producing a high-resolution (HR) image or video from low-resolution images or frames. In this technology, multiple low-resolution (LR) images are applied to generate the single high-resolution image. The image super-resolution is applied in a wide range, including the areas of military, medicine, public safety and computer vision, all of which will be in great need of this technology. The SR process is an ill-posed inverse problem, even though the estimation of HR image from LR input image has many possible solutions. There are many SR algorithms available to resolve this ill-pose problem. Interpolation Based method is the most intuitive method for the image super-resolution. This kind of algorithm has the low-resolution image registered on the grid of the high-resolution image to be calculated. Reconstruction based method is mainly based on iterative back projection method. This algorithm is very convergent, simple and direct, but the resoluti on is not steady and unique. Because of the limitation of the reconstruction algorithm, the learning-based super-resolution technology emerges as an active research area. Learning based approach synthesize HR image from a training set of HR and LR image pairs. This approach commonly works on the image patches (Equal-sized patches which is divided from the original image with overlaps between neighbouring patches). Since, learning based method achieves good performance result for HR image recovery; most of the recent technologies follow this methodology. Freeman et al [1] describe a learning based method for low-level vision problem-estimating scenes from images and modeling the relation between synthetic world of images and its corresponding images with markov network. This technique use Bayesian belief propagation to find out a local maximum of the posterior probability for the scene of given image. This method shows the benefits of applying machine learning network and large datasets to the problem of visual interpretation. Sun et al [2] use the Bayesian approach to image hallucination where HR images are hallucinated from a generic LR images using a set of training images. For practical applications, the robustness of this Bayesian approach produces an inaccurate PSF. To overcome the estimation of PSF, Wang et al [3] propose a framework. It is based on annealed Gibbs sampling method. This framework utilized both SR reconstruction constraint and a patch based image synthesis constraint in a general probabilistic and also has poten tial to reduce the other low-level vision related problems. A new approach introduced by Yang et al [4] to represent single image super-resolution via sparse representation. With the help of low resolution input image sparse model, output high resolution image can be generated. This method is superior to patch-based super-resolution method [3]. Zedye et al [5] proposed a sparse representation model for single image scale-up problem. This method reduces the computational complexity and algorithmic architecture than Zhan [6] model. Gao et al [7] introduce the sparsity based single image super-resolution by proposing a structure prior based sparse representation. But, this model lags in estimation of model parameter and sparse representation. Freedman et al [8] extend the existing example-based learning framework for up-scaling of single image super-resolution. This extended method follows a local similarity assumption on images and extract localized region from input image. This techn ique retains the quality of image while reducing the nearest-neighbour search time. Some recent techniques for single image SR learn a mapping from LR domain to HR domain through regression operation. Inspired by the concept of regression [9], Kim [10] and Ni Nguyen [11] use the regression model for estimating the missing detail information to resolve SR problem. Yang and Wang [12] presented a self-learning approach for SR, which advance support vector regression (SVR) with image sparse co-efficient to make the model relationship between LR and HR domain. This method follows bayes decision theory for selecting the optimal SVR model which produces the minimum SR reconstruction error Kim and Kwon [13] proposed kernel ridge regression (KRR) to train the model parameter for single image SR. He and siu [14] presented a model which estimates the parameter using Gaussian process regression (GPR).Some efforts have been taken to reduce the time complexity. Timofte et al [15] proposed Anchored neighbourhood regression (ANR) with projection matrices for mapping the LR image patches onto the HR image patches. Yang et al [16] combined two fundamental SR approaches-learning from datasets and l earning from self-examples. The effect of noise and visual artifacts are suppressed by combining the regression on multiple in-place examples for better estimation. Dong et al [17] [18] proposed a deep learning convolutional neural network (CNN) to model the relationship between LR and HR images. This model performs end-to-end mapping which formulates the non-linear mapping and jointly optimize the number of layers. An important issues of the example learning based image SR technique are how to model the mapping relationship between LR and HR image patches; most existing models either hard to diverse natural images or consume a lot of time to train the model parameters. The existing regression functions cannot model the complicated mapping relationship between LR and HR images. Considering this problem, we have developed a new image super-resolver for single image SR which consisting of cascaded linear regression (series of linear regression) function. In this method, first the images are subdivided into equal-sized image patches and these image patches are grouped into clusters during training phase. Then, each clusters learned with model parameter by a series of linear regression, thereby reducing the gap of missing detail information. Linear regression produces a closed-form solution which makes the proposed method simple and efficient. The paper is organized as follows. Section II describes a series of linear regression, results are discussed in section III and section IV concludes the paper. Inspired by the concept of linear regression method for face detection [19], a series of linear regression framework is used for image super-resolution. Here, the framework of cascaded linear regression in and how to use it for image SR were explained. A. Series of Linear Regression Framework The main idea behind cascaded linear regression is to learn a set of linear regression function for each cluster thereby gradually decreasing the gaps of high frequency details between the estimated HR image patches and the ground truth image patches. In order to produce the original HR image from LR input image, first interpolate LR image to obtain the interpolated LR image with same size as HR image. This method works at the patch level, each linear regressor parameter computes an increment from a previous image patch, and the present image patch is then updated in cascaded manner. (1) (2) denotes the estimated image patch after t-stages. denotes the estimated increment. denotes feature extractor by which the f-dimensional feature vector can be obtained. Linear regressor parameters at t-stage T Total number of regression stages. The next step is learning of the linear regression parameters and for T stages. Relying on these linear regression T stages, parameters for regressors are subsequently learnt to reduce the total number of reconstruction errors and to make presently updated image patch more appropriate to generate the HR patch. Using least squares form to optimize and , it can be written as, (3) The regularization term accomplishes a constraint on the linear regression parameters and to avert over-fitting and ÃŽÂ ² be the data fidelity term and the regularization term. At each regression stage, a new dataset values can be created by recurrently applying the update rule in (1) with learnedand. Next, and can be learned subsequently using (2) in cascade manner. Fig. 1. Flow of cascaded linear regression framework B. Pseudo code For Cascaded Linear Regression Algorithm The Pseudo code for cascaded linear regression algorithm for training phase is given below, Input: , image patch size à ¢Ã‹â€ Ã… ¡d xà ¢Ã‹â€ Ã… ¡d for t=1 to T do { Apply k-means to obtain cluster centres for i = 1 to c do { compute A and b. update the values of A and b in . } end for } end for The output of this training phase is and cluster centroid. C. SERF Image Super-Resolver This section deals with cascaded linear regression based SERF image. The process starts by converting color image from the RGB space into the YCbCr space where the Y channel represents luminance, and the Cb and Cr channels represent the chromaticity. SERF is only applied to the Y channel. The Cb and Cr channels reflect G and B channels of the interpolated LR image. D. SERF Implementation To extract the high frequency details from each patch by subtracting the mean value from each patch as feature patch denoted as . Since the frequency content is missing from the initially estimated image patches, the goal of a series of linear regression is to compensate for high frequency detail (4), (4) To diminish the error between HR feature patch and the estimated feature patch, it is normal that the regression output should be small. Hence, by putting the constraint on regularization term to (4), the output is, (5) Where, ÃŽÂ » is the regularization parameter. t Denotes the number of regression stages. denotes the feature extractor. ÃŽÂ ² and ÃŽÂ » are set to 1 and 0.25. A closed-form solution for equation (5) can be computed by making the partial derivative of equation (5) equal to zero. In testing phase, for a given LR image, bicubic interpolation is applied to up sample it by a factor of r. This interpolated image is divided into M image patches. Feature patches are calculated by subtracting the mean value from each image patch. At the tth stage, each feature patch is assigned to a cluster l according to the Euclidean distance. To obtain the feature subsequently, linear regression parameters are applied to compute the increment. Concurrently, the feature patch is updated using, (6) After passing through T-stages, reconstructed image patches are obtained by adding mean value to the final feature patches. All the reconstructed patches are then combined with the overlapping area and then averaged to generate the original HR image. E. Pseudo code For SERF Image Super-Resolver Algorithm The pseudo code for SERF image super-resolver algorithm is as follows: Inputs: Y, a, r, for t=1 to T do { Adapt each patch clusterto a cluster. Compute. Update the values of A and b in } End for The output will be the High Resolution image (HR). The simulation of the SERF image super-resolver is done by using MATLAB R2013a for various images. The LR image is read from image folder and is processed using the algorithms explained before. The output HR image is taken after regression stages. The implementation is done by considering many reference images. The colour image (RGB) is first converted into YCbCr space, where Y channel represents luminance. Cb and Cr are simply copied from the interpolated LR image. The number of cluster size is 200. Image patch size 5 x 5 and magnification factor is set to 3. a)LR input b)HR input (c)Zooming result Fig.2. SERF Result under Magnification Factor 3 a)LR input b)HR output c)zooming result Fig.3. SERF Result under Magnification Factor 2 a)LR input b)HR output c)zooming result Fig.4. SERF Result under Magnification Factor 1 (a) (b) (c) (d) (e) (f) (g) (h) Fig.5. Comparisons ResultsButterfly (a) ground truth image (original size is 256 ÃÆ'- 256); (a)super-resolution results of (b) SRCNN, (c) ScSR, (d) Zeydes, (e) ANR, (f) BPJDL,(g) SPM, and (h) SERF. Zeydes [5] method gives noiseless image, but texture details are not well reconstructed as shown Figure (d). The BPJDL [14] methods generate sharper edges when compared to other methods as shown Figure (f). Figure (h) shows the zooming results of SERF method that performs well for both reconstruction and visual artifacts suppression. TABLE I:PSNR AND SSIM VALUES UNDER MAGNIFICATION FACTOR OF 1, 2 AND 3. Magnification Factor PSNR SSIM TIME(s) 3 29.0775 0.839 0.4323 2 30.5 0.812 0.4000 1 38.4 0.798 0.3870 TABLE II:PSNR AND SSIM VALUES UNDER MAGNIFICATION FACTOR OF 3 FOR TESTING IMAGES. S.NO IMAGES PSNR SSIM TIME(s) 1 Baboon 23.63 0.532 0.3115 2 Baby 35.29 0.906 0.4148 3 Butterfly 26.87 0.883 0.2018 4 Comic 24.32 0.755 0.2208 5 Man 28.19 0. 778 0.5468 6 zebra 29.09 0.839 0.4324 For magnification factor of 3, SERF outplays ScSR method by an average PSNR gain of 0.43dB, Zeydes [5] method by 0.37dB, ANR [15] by 0.44dB, BPJDL [14] method by 0.23dB and the SPM [7] method by 0.16dB. SERF gives average SSIM value of 0.8352 and it is fastest method compared to existing methods (TABLE III). TABLE III: PSNR AND SSIM VALUE COMPARISON OF SERF METHOD WITH EXISTING METHODS UNDER MAGNIFICATION FACTOR OF 3. EXISTING METHODS PSNR SSIM TIME(s) ScSR [4] 23.69 0.8835 7.27 Zeydes [5] 23.60 0.8765 0.06 ANR [15] 24.32 0.8687 0.02 BPJDL [14] 24.17 0.8890 17.85 SPM [7] 24.63 0.8982 0.74 SERF 29.0775 0.8352 0.23 SERF has few parameters to control the model, and results in easy adaption for training a new model when the experimental settings, zooming factors and databases were changed. The cascaded linear regression algorithm and SERF image super-resolver has been simulated in MATLAB2013a. SERF Image super-resolver achieves better performance with sharper details for magnification factor up to 3. This model reduces the gaps of high-frequency details between the HR image patch and the LR image patch gradually and thus recovers the HR image in a cascaded manner. This cascading process promises the convergence of SERF image super-resolver. This method can also be applied to other heterogeneous image transformation fields such as face sketch photo synthesis. Further this algorithm will be implemented on FPGA by proposing suitable VLSI architectures. REFERENCES [1] W. Freeman, E. Pasztor, and O. Carmichael, Learning low-level vision, International Journal of Computer Vision, vol. 40, no. 1, pp. 25-47,2000. [2] J. Sun, N. Zheng, H. Tao, and H. Shum, Image hallucination with primal sketch priors, in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2003, pp. 729-736. [3] Q. Wang, X. Tang, and H. Shum, Patch based blind image super resolution, in Proceedings of IEEE international Conference on Computer Vision, 2005, pp. 709-716. [4] J. Yang, J. Wright, T. Huang, and Y. Ma, Image super-resolution via sparse representation, IEEE Transactions on Image Processing, vol. 19,no. 11, pp. 2861-2873,2010. [5] R. Zeyde, M. 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Gool, Anchored neighborhood regression for fast example-based super-resolution, in Proceedings of IEEE Conference on Computer Vision, 2013, pp. 1920-1927. [16] J. Yang, Z. Lin, and S. Cohen, Fast image super-resolution based on in-place example regression, in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2013, pp. 1059-1066. [17] C. Dong, C. Loy, K. He, and X. Tang, Learning a deep convolutional network for image super-resolution, in Proceedings of European Conference on Computer Vision, 2014, pp. 184-199. [18] C. Dong, C. Loy, K. He, and X. Tang, Image super-resolution using deep convolutional networks, IEEE Transactions on Pattern Analysis and Machine Intelligence, DOI:10.1109/TPAMI.2015.2439281, 2015. [19] P. Viola and M. Jones, Robust real-time face detection, International Journal of Computer Vision, vol. 57, no. 2, pp. 137-154, 2004.

Childhood Obesity: The Causes And Health Problems Essay

The topic that I have chosen for my paper is addressing the issue of childhood obesity. According to Merriam-Webster (2010) obesity is a condition where there is excessive accumulation and storage of fat in the body. I think that childhood obesity is an epidemic that the United States of America is facing as a major issue for children health. Childhood obesity is one of the fastest growing health concerns in the United States. The definition of an epidemic according to Encyclopedia Britannica (2011) is the occurrence of disease that is temporarily of high prevalence. The childhood obesity rate has more than tripled in the United States over the last 30 years( Center for Disease Control, 2010) I will prove that there are ways to prevent the causes of childhood obesity. Research will be focusing on the causes, the health concerns, and the ways to prevent childhood obesity. The resources that will be using for the research are: internet research, case and research reports, and interviews. So far research findings are showing that there are many causes and effects of childhood obesity. Childhood obesity is difficult health problem because it has biological, behavioral, social, economic, environmental, and cultural causes (Koplan, Liverman, Kraak, 2005, pg 340). Each one of the causes stated above can have many different influences for a child in this country. An economic example is when the recession caused a lot of people to lose their jobs. The recession caused a lot of people to lose their income, which caused a lot of families to not be able to afford the necessities of life. People had to choose between making their house payment or buying food. The number of people also has something to do with how a family eats. The number of people that are in the family determines the amount and the cost of food needed. A family income also has an important role in a family. If the family size is larger and the income level is low then the  family may have problems buying healthier food. A social example would be their peers influencing what they eat when they are away from their house. Children could be more likely to eat what their friends eat when they are not home as well. The community has a role in this as well. The reason the community has a role in this is due to the restaurants that are in the area and the food those places offer. If your community only offers fast food then the child is more likely to eat that food, which is high in fat. The education level and number of parents may also affect childhood obesity. Research shows that the higher level of education and if both parents are in the home then it is less likely that a child will be obese ((Koplan, Liverman, Kraak, 2005, pg 216). A cultural example would be how we advertise products in the United States. Companies are advertising towards children. For example at a fast food restaurant the child meal comes with a toy. With children watching more and more TV these days the retailers are making commercials for the children that are watching them. The child’s ethnicity is an important factor as well. The traditions that the parents grow up with will be taught to the child and passed down to the next generation. The community again is important factor for this cause as well. People adapt to where they are living. For example if a family moves to from a neighborhood that had more restaurants than fast food, and then the family moves to a neighborhood that has nothing but fast food and there is no other place to eat they may change their diet. A behavioral example would be that a young child that does not want to eat their vegetables at lunch because they say they do not like them. Portion size is a very important factor for childhood obesity. If the portion size of an unhealthy food is too big then the child is more likely to become overweight. When a child is eating fast food or processed food or if they are drinking soda and juice, then they are consuming more sugar and calories than someone that may be eating vegetables or fruit. When a child intakes more calories or sugar, then does not exercise then the child is not burning off the extra intake off. Some children also play a lot of video games and  a child may watch a lot of TV, instead of playing outside. A biological example is if someone has a medical condition where it makes their metabolism not work the best and it causes them to have trouble losing weight or they just gain weight easily. Every person has a different metabolism level as well. Some people may have an over active metabolism and then there are others that have a metabolism that is very slow. Metabolism is how fast the body burns off calories or energy that the body takes in. Another factor is genes or heredity. Genes or heredity is something that you get from your parents of other people in your family. This gets passed down from generation to generation and there is nothing that can be about it, except trying to offset it buy changing diet or level of physical activity. An environmental example would be looking at the ease of accessing processed or fast food near a child’s home. The food that a parent fixes their child is an important factor. This is important due to the fact if the parent is fixing processed or just bringing fast food home then the child will be in taking more calories per meal which could affect the child’s weight in a negative way. The foods that a school offers are also a factor. A school that offers healthier choices may help the student choose the healthier food. When a child chooses a healthier food then they are more likely to continue their diet as an adult but this will also help the child’s weight then too. The state that a child lives in could also be a factor. Each state has their own specialty that almost everyone fixes. If the child is living somewhere, where foods are fired then they are in taking more fat and calories. There are so many health concerns that come with childhood obesity. Some of the health concerns are type 2 diabetes, cardiovascular disease, high blood pressure, lower life expectancy, stress, depression, and low self-esteem. According to the Center of Disease Control (2010) children that suffer from childhood obesity are more likely to get the above health problems when they get older to have these diseases as adults. If some kids are left to manage their own health then they will be unhealthy as an adult because their  lifestyle will not change. This is a very important health concern for our children because they are the future leaders of this country and this is causing them to die earlier than their life expectancy. Cardiovascular disease is one disease that is affecting children with childhood obesity. Cardiovascular disease is relating to or involving the heart and blood vessel in the body(Merriam-Webster, 2011). According to the Center of Disease Control, 70% of children that are obese have at least one risk factor for cardiovascular disease. One contributing factor is cholesterol to the above statistic. Kids are eat more and more fast food in this generation which is increasing their weight and cholesterol levels. Children are also not exercising or doing cardiovascular exercises like: swimming and running. This disease can also lead to many other health problems in adulthood. Another health concern for children that are obese is high blood pressure. Keeping your blood pressure in a healthy level is important because high blood pressure can lead to a heart attack or stroke. High blood pressure is known as hypertension. Hypertension is abnormally high arterial blood pressure (Merriam-Webster, 2011). According to Rob Stein (2007) â€Å"increases so far have been small — just 2.3 percentage points for early hypertension and 1 point for full-blown hypertension — they translate into hundreds of thousands more children developing what often becomes a chronic, lifelong condition†. High blood pressure can lead to other health concerns such as: heart disease, stroke and kidney troubles. The next major health issue that can come from a child being obese is type 2 diabetes. According to the University of Michigan Health System it could take up to 10 years before an obese child shows the development of type 2 diabetes. Type 2 diabetes is when the body can not process the insulin in the body produces. There are many things that can contribute to a child having type 2 diabetes. The level of exercise can help lower the risk of type 2 diabetes because when you exercise it helps the body burn the energy that is consumed. There are some complications for health when you have diabetes, those complications are blindness and amputations of the arms  and/or legs. Stress can also have an effect on a child’s health. Stress can cause many health issues because it takes energy to be stressed and many people worry when they are stressed. According to Merriam-Webster (2011), stress is a physical, chemical, or emotional factor that causes bodily or mental tension and may be a factor in disease causation. Stress can come from home, school, friends, and family. When someone is stressed they may eat for comfort and this can cause extra calories being taken in and not burned off. Stress can be linked to heart disease, stroke, high blood pressure, and many more. Low self-esteem is another important factor for children. Low self-esteem is a confidence and satisfaction in oneself. Low self-esteem can be from children bullying, making fun of each other, and stress. When a child has trouble making friends they may form a low self-esteem. Low self-esteem can cause an eating disorder. According to Susan Okie (2005, p 73), obesity is a risk factor for the development of an eating disorder. A child overweight could have a poor self-image and that could lead to an eating disorder. There is also a lower life expectancy with children that are obese. Life expectancy is referring to the age that a person lives to base on sex, ethnicity, and other factors. There are many factors that contribute to this. One factor is suicide. Children that have a low self-image or ones that have given up could resort to suicide. Another factor is the health issues that the obese child has. As the child gets older and if they already have heart disease, high blood pressure, or type 2 diabetes are likely to have the complications with those diseases earlier in their adult life. One way that Americans are trying to prevent childhood obesity is having schools offer more gym class time. Another way that Americans are trying to incorporate exercise is that the TV network Nickelodeon has a day that they turn off the cartoons and encourage children and their parents to go outside and play, they call it the national day of play. There are many health  programs also ran by the government trying to help low income families with providing their children with a balanced diet. The biggest thing that a parent can do is change their own lifestyle and pass it to the child. A parent leads by example showing their children how to live, act, and what to believe. A parent can start a daily activity with the family included and make this routine for the family. The parent can also change the food bought at the store and what is fixed at home. A child can be given a much smaller portion size and then if they are still hungry they could ask for more and the parent would determine the portion size of the extra the child would get. School are not out of the woods on this subject matter either. Schools offer processed food which are higher in calories, fat, and maybe sugar. Schools need to change their menu to offer healthier lifestyle choices. Schools would also help prevent childhood obesity by increasing the amount of time the student gets physical exercise during the school day. Another item the school should change to help prevent childhood obesity is the portion size of food their offering to a child. A community could ban together to prevent childhood obesity as well. The community could offer to have a campaign. These campaigns would be able to use multiple strategies,such as: media campaigns , community mobilizations, education programs for health professionals and the general public, modifications of physical environments, and health screenings and referrals (Koplan, Liverman, Kraak, 2005, pg 196). The community could also build a bike or walking trail in a park. Having the bike or walking trail will encourage the citizens of the community to get out and exercise. The community could also build a community center that has exercise equipment or a program ran thru the building to help teach parents to eat right. When the parents know what to do to help then they can pass the information down to the children. There are also many government ran programs to help low income families. One good example is the WIC program which stands for Women, Infant, and  Children. This program helps low income people that are pregnant or they have children under the age of five and they also have to meet the income guidelines for the size of the household. Income guidelines vary depending on what state in which the family resides in. WIC only offers certain foods to families. They offer formula for a baby, but the formula has to be on a state approved list (USDA, 2011). If there is a pregnant woman in the household, WIC then offers the household milk, whole grain bread, peanut butter or dry beans, fruit, approved cereal, and 100% fruit juice(USDA, 2011). If the woman is breastfeeding the baby then she is offered tuna and carrots for the nutritional value those foods give (USDA, 2011). Another government program is Food Stamps which help low income families buy food. There are income guidelines that someone would have to meet before getting assistance with this program. There is not much of a requirement of what food to buy on this program but they do have items that you are not allowed to buy. The recipient cannot buy taxable items such as diapers, paper towels, or toilet paper. The recipient cannot buy alcohol either. When a person on food stamps goes to a store to buy groceries the register will notify the cashier what is food stamp eligible and what is not. Anything that is not eligible the consumer must pay for. Without these programs the United States could have lots of hungry people that could starve to death. Research is stating that childhood obesity is preventable when proper diet and exercise are incorporated into a child’s life from a young age. If a child does not eat healthy foods and does not exercise then that child is more likely to be obese. When a child is obese, then the child is at risk for some serious medical diseases that may not show up until they are an adult. If a child has a serious medical disease young then they more likely to have a more severe condition like cardiovascular disease when they are an adult. There are many government and community programs to help the low income families that are in need of assistance. Preventing childhood obesity is a concern for parents, schools, communities, and government to solve together with all of the programs available. References Center of Disease Control. (2010, June 3). Childhood Obesity. In Health Topics (par. 2) [Fact Sheet]. Retrieved from http://www.cdc.gov/healthyyouth/obesity/#5 Epidemic. (2011). In Encyclopedia Britannica. Retrieved from http://www.britannica.com/EBchecked/topic/189776/epidemic Institute of Medicine (U. S.) Committee on Prevention of Obesity in Children and Youth. (2005). Confronting the Childhood Obesity Epidemic. In V. A. Kraak, J. P. Koplan, & C. T. LIverman (Eds.), Preventing Childhood Obesity: Health in the Balance. (p. 196). Retrieved from http://site.ebrary.com/lib/ashford/Doc?id=10075881&ppg=196 Institute of Medicine (U. S.) Committee on Prevention of Obesity in Children and Youth. (2005). Confronting the Childhood Obesity Epidemic. In V. A. Kraak, J. P. Koplan, & C. T. LIverman (Eds.), Preventing Childhood Obesity: Health in the Balance. (p. 216). Retrieved from http://site.ebrary.com/lib/ashford/Doc?id=10075881&ppg=216 Institute of Medicine (U. S.) Committee on Prevention of Obesity in Children and Youth. (2005). Confronting the Childhood Obesity Epidemic. In V. A. Kraak, J. P. Koplan, & C. T. LIverman (Eds.), Preventing Childhood Obesity: Health in the Balance. (p. 340). Retrieved from http://site.ebrary.com/lib/ashford/Doc?id=10075881&ppg=340 Merriam-Webster Dictionary (Ed.). (2011). Merriam-Webster. Retrieved from http://www.merriam-webster.com/medical/cardiovascular Merriam-Webster Dictionary (Ed.). (2011). Merriam-Webster. Retrieved from http://www.merriam-webster.com/dictionary/hypertension Merriam-Webster Dictionary (Ed.). (2011). Merriam-Webster. Retrieved from http://www.merriam-webster.com/dictionary/obesity?show=0&t=1297741121 Merriam-Webster Dictionary (Ed.). (2011). Merriam-Webster. Retrieved from http://www.merriam-webster.com/dictionary/self-esteem Merriam-Webster Dictionary (Ed.). (2011). Merriam-Webster. Retrieved from http://www.merriam-webster.com/medical/stress Merriam-Webster Dictionary (Ed.). (2011). Merriam-Webster. Retrieved from http://www.merriam-webster.com/dictionary/type+2+diabetes?show=0&t=1298313788 Okie, S. (2005). Size, Health, and Self Esteem. In Fed Up! Winning the War Against Childhood Obesity (pp. p 73). Retrieved from http://site.ebrary.com/lib/ashford/Doc?id=10075869&ppg=73 Stein, R. (2007, September 17). More Kids Developing High Blood Pressure. The Washingtion Post, par 3. Retrieved from: http://www.washingtonpost.com/wpdyn/content/atricle/2007/09/10/AR2007091001349.html?hpid=topnews University of Michigan Health System (2008, July 12). Coming Epidemic Of Type 2 Diabetes In Young Adults. ScienceDaily. Retrieved fromhttp://www.sciencedaily.com/releases/2008/07/08070819329.htm USDA. (2011). Women Infants and Children [Brochure]. Retrieved from http://www.fns.usda.gov/wic

Act Essay Scoring Samples: the Ultimate Convenience!

Act Essay Scoring Samples: the Ultimate Convenience! The Downside Risk of Act Essay Scoring Samples Otherwise, an essay receives an overall score depending on the domain scores awarded by the 2 readers. It shows a good command of language. Your essay doesn't have to DO ALL THE THINGS in every single category to be able to be given that score. Your basic five-paragraph essay begins with the introduction. This fact can further affect the variability. If you're still on the fence about whether to select the ACT whatsoever, and take the SAT instead, comparing both essays might provide help. A standard question regarding SAT scores is whether the entire mess can be prevented by skipping the essay. Take a little time to reread your overview of the prompt. Ideas are logically sequenced, although easy and obvious transitions could be used. Organization Students are given a score for the way that they organize their essay. Transitions are rarely employed. There are hundreds and hundreds of successful methods to approach this essay. Most ideas are totally elaborated. Instead center on completing the essay, ensuring it comprises every important key notion, some support for every one of the critical ideas, and an obvious conclusion. Regrettably, the ideas they give are a little obtuse. Make sure you get every point you have earned. There's no one-size-fits-all reply to that question. You don't have to restate every argument you've made within the body, but you ought to summarize your argument and restate your thesis in various words. You have to evaluate three distinct arguments, you should come up with your own argument, and after that you must relate your argument to the 3 arguments given. Not just that, but nevertheless, it will be harder to compare your essay to others. The essay shows minimum comprehension of the job. It shows a clear understanding of the task. The ACT essay was always simple to master with a little bit of practice and the perfect practices. It grants you the possiblity to rate your own skills even before the test, which means you'll know which weak regions to concentrate on as soon as you get started reviewing. It is not important to us, whether you're too busy on the job concentrating on a passion undertaking, or simply tired of a seemingly infinite stream of assignments. Even in the event the deadline is very tight, feel free to get hold of our managers. The very first step in calculating a score for a subject area test is to learn the raw score for this test. It's safe to presume that this is going to be the exact same in every subsequent ACT Writing test. ACT insists this isn't unexpected or an indication of an issue with the new test. In fact, the ACT is doing all the effort for you! Have a look at our best-in-class on-line ACT prep program. There's also a possibility that the new scoring session could find exactly the same result a second moment. It is preferable to have a less complicated structure which is used correctly versus an effort at a more advanced grammatical concept that is in fact erroneous. The way to acquire an outstanding ACT writing score is to create the graders' jobs easy. Learn ways to get your ACT target score, step-by-step.

An Investigation Of An Assault - 1019 Words

INVESTIGATION The complainant gave over all the email correspondence to the police. A number of them had cover headers and accordingly the police couldn t examine them any further. Other than there was no email that could be taken after to Kolkata where the charged was staying as indicated by the complainant s version. However the examining group could follow some of these messages to the corporate office of a substantial concrete organization and a habitation in Mumbai. An assault was directed at these premises. In the attack one PC, two portable workstations, seven cellular telephones and a scanner were seized. The PC gear that was recovered was sent to the workplace of the criminological analyst, who discovered every one of the†¦show more content†¦Use Strong Passwords Use distinctive client ID/watchword mixes for diverse records and cease from recording them. Make the passwords more blurred by consolidating letters, numbers, unique characters (least 10 characters altogether) and change them all the time. 2. Secure your PC a. Initiate your firewall: Firewalls are the first line of digital protection; they piece associations with obscure or fake locales and will keep out a few sorts of infections and programmers. b. Use against infection/malware programming: Keep infections from installing so as to blemish your PC and frequently repair unfavorable to infection programming. c. Block spyware assaults: Keep spyware from installing so as to penetrate your PC and upgrading hostile to spyware programming. 3. Be Social-Media Savvy: Ensure your person to person communication profiles (e.g. Facebook, Twitter, Youtube, MSN, and so forth.) are set to private. Check your security settings. Be cautious what data you post on the web. When it is on the Internet, it arrives until the end of time! 4. Secure your Mobile Devices: Know that your cell phone is defenseless against infections and programmers. Download applications from trusted sources. 5. Introduce the most recent working framework updates: Keep your applications and working framework (e.g. Windows, Mac, Linux) current with the most recent framework overhauls. Turn on programmed overhauls to protect potential assaults on more established programming. 6.