The Production Function for Buses - Edgeworth Box Assignment

The Production Function for Buses - Edgeworth Box - Assignment Example Our function will reproduce increasing returns to scale. This means that with an accumulation of production factors volume of produced goods will grow. To find a number of buses with every combination of production factors, it is necessary to substitute each number of employees and the number of machines for K and L indicators. Hence, if a number of machines are 14 and number of employees who make buses is 5, the calculation of production output will be the following: In accordance with above example, we can calculate all the rest level of production. (K=10, L=3): (K=8, L=1): etc. From the table, we can also see that in accordance with the accumulation of employees, the number of produced buses grows. Part (B) Make an ‘Edgeworth box’ diagram for the production of buses in Utropica: put the number of employees making buses on the horizontal axis (0 to 6), and a number of machines used to make buses on the vertical axis (0 to 16). Draw an isoquant line for 5 buses. On the same diagram, add an isoquant for 7 buses, and an isoquant for 10 buses. To draw an ‘Edgeworth box’ diagram for the production of a specific number of buses, it is required to find all combinations of factors that are able to create the stated level of production. Hence, using a table above, it can be seen that 5 buses can be produced by 10 machines and 1 employee or 8 machines and 2 employees. So there are several alternatives for this output. Consequently, finding all possible combinations, we receive points that will form the isoquant line on the diagram. Using the same method, we find combinations of the factors for producing 7 buses.

Content-based Image Retrieval (CBIR) System

Content-based Image Retrieval (CBIR) System Chapter 1. Introduction Nowadays, in the most of areas it is necessary to work with large amounts of growing visual and multimedia data, at the same time, the number of image and video files on the web is quite big and is still rising very rapidly. Searching through this data is absolutely vital. So, there is a high demand on the tools for image retrieving, which are based on visual information, rather than simple text-based queries. Content-based Image Retrieval (CBIR) consists of retrieving the most visually similar images to a given query image from a database or group of image files. It is a quite useful thing in a lot of areas such as Photography which may involve image search from the large digital photo galleries; Medicine it is used to assist in diagnosis. In most of diseases, their visual characteristics carry diagnostic information and visually similar images correspond to the same disease category. The output of a CBIR system can help to make a decision (Tahmoush, 2007); Military detection of e nemy soldiers or vehicles from screen photographs; Crime prevention it helps police in suspicious peoples identification from large image databases and in image retrieval of crime scene photos (Wen, 2005); Geography frequently used in Geographical information systems (GIS) (Hafiane, 2006) and many others. CBIR has been a subject of intense research over the last 15 years. It is one of the most difficult research areas in multimedia computing and information retrieval. During the research history many different image matching, indexing and retrieval algorithms have been tried. Practice shows that user queries described by visual information are more effective and more precisely meet user needs, than standard text search queries. It is because visual information is closer to the humans perception of the world. 1.1 CBIR Systems Many CBIR systems and tools have been developed to make queries based on visual content. During the 90-ies several notable commercial systems were introduced. IBM developed Query By Image Content (QBIC) system, which lets user to make queries of large image databases based on visual image content properties such as Example images; User-constructed sketches and drawings; Selected color and texture patterns. (Flickner, 1995) Soon after that â€Å"Virage Image Search Engine† of Virage Inc. was developed, which provides an open framework for building systems that explicitly manages image assets by directly representing their visual attributes. (Bach, 1996) Several online content-based web search engines can also be mentioned. â€Å"WebSEEk† developed by Image and Advanced Television Lab, Columbia University. It allows making queries by example and by desired color composition. â€Å"Chabot†, Developed by Department of Computer Science, University of California, which allows to search by colors, but offers limited options such as choosing one dominant color. (Veltkamp, 2002) Global Memory Net (GMNet) was launched for public access in late June 2006. It is a digital library of cultural, historical, and heritage image collections. Among other text-based searching types this web library has a possibility to search by image content. It has two basic options for content based searching. Search by example image, based on its color and shape and by user drawing. For CBIR, GMNet uses SIMPLIcity developed by Prof. James Z. Wang of Penn State University. (Chen 2006) Different CBIR systems use different types of user queries. Typically tools for the content-based image retrieval consist of query statement and a result presentation; this query can be done by providing an example image a sketch, or by choosing desired colors for the image. Results are presented by the top several similar images based on the similarity measure. 1.2 Research Questions Despite the large number of CBIR systems developed, there are still a lot of challenging problems in this area. The important sides that still need to be improved are speed of retrieving, when working with the large databases, accuracy and effectiveness of the retrieved results. So the researchers from multiple disciplines are deeply concerned with these aspects. Comparisons by image content are much more complicated task than by textual data. Generally, content-based image retrievals are based on comparison of image content descriptors that represent visual features of the image. Different features can be used to obtain the image descriptor. To meet specific user needs and in various cases some of them are more effective than others. Sometimes the implementation simplicity is as important as retrieval accuracy and effectiveness. Based on the previous discussion, research questions are the following: What are the basic retrieval techniques? What kind of features are usually used? How the features are obtained from the image? How these features are matched? How the retrieval results are presented to the user? How accurate can be the algorithms, which are relatively easy to implement? 1.3 Objectives The CBIR research often involves two areas computer vision and database systems. The database systems part studies database indexing, searching and retrieval techniques and computer vision part is about image processing, obtaining the image descriptors and image matching. In order to answer the research questions this dissertation focuses on a computer vision part. Image processing and image transformations are used by CBIR systems in order to extract image descriptors. CBIR systems are based on different image features descriptors matching. Some of these systems perform image comparison by multiple features at the same time and some of them use only one feature. In this dissertation we are going to investigate what are the basic techniques used in CBIR systems, which are based on different feature descriptors. We will make a detailed overview of these basic methods. We are also going to implement one of the most effective algorithms in the CBIR field. This is Scale Invariant Feature Transform (SIFT) algorithm (Lowe, 2004) and see how effective and accurate it can be. Chapter 2. Literature Survey 2.1 CBIR systems typical architecture Typical CBIR system has two main functionalities. This is Data insertion and query processing. Data insertion procedures are performed independent of user interaction.   They are applied to all the data. The purpose of this process is to extract visual features from the images in the database. These features are obviously smaller than the actual image and they are then stored for easy comparison reasons, as a characterizers of each image. Query processing starts with user specific request. Request can be done in several ways: By an example image, by giving desired pattern or object, color distribution and etc. Query processing module obtains the visual features from the given request, metric is defined. Then similarity is measured based on the chosen metric and some set of the most similar images are . Features extraction itself involves, selecting the features that have to be extracted, it depends on the type of user query. The feature extracting algorithm is chosen to create the feature vector from the selected features. Eventually, image descriptor is formed which are then used to compare the images. (Torres, 2006) 2.2 Semantic Gap Basically, similarity searching between the images is based on low-level and higher-levels of queries. (Eakins, 1996) Low-Level Similarity in this case visual features to describe the image are primitives such as color, texture and shape. Higher-Levels, Semantic Similarity at higher levels, similarity searching is not based on a simple features. In this case images are described by higher level of semantic attributes. This involves identification of the object types depicted in the image. These two levels of queries form the problem called semantic gap. Semantic gap can be defined in the following way: â€Å"The semantic gap is the lack of coincidence between the information that one can extract from the visual data and the interpretation that the same data has for a user in a given situation.† (Datta, 2008) In another words, images with high low-level feature similarities may still be different in terms of user perception. So similarity by low-level features, not always mean semantic similarity of these images. 2.3 Content Comparison Techniques This dissertation is concerned with low-level similarity features extraction .CBIR for low-level similarity queries needs techniques which can be used to obtain the image content descriptors to compare images based on their color, texture and shape. Color Image content comparison by color is based on matching images by their color distribution. In this case image feature identifies the proportion of pixels of specific color or colors within an image. So one can make color searches by indicating desired concentration of colors or by an example image with desired color distribution and get similar images. Color histograms are widely used to extract the color distribution descriptors from the image. It is a statistic of the color of pixels in the image. First color distribution is represented by appropriate color histogram, and then color vector is formed from that histogram. Lets discuss several color feature extraction histograms. Conventional Color Histogram (CCH) This histogram consists of occurrences of each color in the image. Each pixel is associated to only one its own histogram bin only on the basis of its own color. This color histogram uses the probability mass function of the image pixel intensities. (Suhasini, 2009) Fuzzy Color Histogram (FCH) as an opposite to CCH, in FCH each pixel is associated to all bins of histogram with different degrees of membership depending on color similarity of the pixel. This is done by fuzzy-set membership function. (ferone, 2008) Color Correlogram (CC) color correlogram of an image is a table which is indexed by color pairs, where the d-th entry of (i,j) cell shows the probability of finding the color j at a distance of d from a pixel of color i in the image extracting. Such a feature from the image is tolerant to the changes in appearance of the same scene which can be caused by changing the viewing positions, but color correlogram is more difficult to compute than color histograms. (Huang, 1997) Texture Retrieval by image texture in a similar to color-based feature extraction, but it looks for visual patterns in images rather than colors. So it looks at homogeneity that is not a result of a single color presence or intensity of a pixel value. Sometimes it also provides more spatial information. The most basic method used to extract the texture descriptor from the image is based on Fourier Transform. The initial image is transformed by the Fourier function. As the method works on digital images, Discrete Fourier Transform (DFT) is used. DFT converts images from the spatial domain into the frequency domain, where all the spatial frequencies of the original image are represented. In another words this transformed image shows intensity variations over a number of pixels. Transformed data is grouped to obtain several measures from it. Then descriptor is formed of these measures and is used for comparison. (Nixon, 2007) Shape Shape-based image retrieval comparison looks at shapes of regions within an image and searches for the shapes similar to given as in a query image. Edge and blob detections are important parts for the shape feature extraction. These edges and blobs are points or regions in the image that are either brighter or darker than the surrounding. Several methods are used for shape-based image retrieval, which involve different kind of image filtering and image transformations. One of the most effective algorithms for shape-based image retrieval is Scale Invariant Feature Transform (SIFT) algorithm, which was first developed by David Lowe in 1999, at the University of British Colombia. It takes a single image as an input and returns a set of detected image features. In SIFT algorithm image filtering is based on Gaussian function. After image filtering SIFT uses Difference of Gaussian (DoG) pyramid for blob (keypoint) detection. The image feature descriptor, which is called keypoint descriptor is 128 element feature vector and formed of gradient magnitudes and orientations computed for the area around the identified keypoints. (Lowe, 2004) Chapter 3. Research Method 3.1 Research approach Mathematical methods play key role in the most of CBIR algorithms. Often mathematical solution of the problem is difficult or impossible to implement practically, therefore it is important to assess the method in practice. Thats why Experimental approach will be used in this dissertation. This method of primary research forces to experience and overcome all the difficulties that can appear during the practical implementation of theory. It requires focusing on the details of algorithm and clearly shows advantages and disadvantages of the particular algorithm. It also gives possibility to assess the instruments used in experiment, which are not less important than algorithm itself. In this dissertation, one of the CBIR algorithms for shape-based image retrieval will be implemented for a number of images and the results will be assessed 3.2 Tools and Technologies used This study focuses on the algorithm which involves image processing. It will be implemented under the Microsoft .net framework platform and using GDI+ and C# programming language. .Net framework provides managed interface for GDI+; therefore its relatively easy to process images using this platform. Microsoft Visual Studio .Net will be used as an IDE. This experiment will also show how useful can be .net framework library and C# language for image processing purpose. References: Bach J., Fuler C., Gupta A., Hampapur A., Horowitz B., Humphrey R., Jain R., Shu C., (1996) The virage image search engine: An open framework for image management SPIE Conference on Storage and Retrieval for Image and Video Databases; Chen Ch. Ch. (2006),Using Tomorrows Retrieval Technology to Explore the Heritage: Bonding Past and Future in the Case of Global Memory Net; available at: http://ifla.queenslibrary.org/IV/ifla72/papers/097-Chen-en.pdf last accessed on 24th September 2009 Datta R., Joshi D., Li J. and Wang J. Z. (2008) Image Retrieval: Ideas, Influences, and Trends of the New Age. Eakins J.P. (1996) Automatic image content retrieval are we getting anywhere?Department of Computing, University of Northumbria at Newcastle, available at: http://www.cs.uu.nl/docs/vakken/mir/materials/literature/eakins.pdf last accessed on 24th September 2009 Ferone A., Maddalena L., Petrosino A., (2008) The Enhanced Color Histogram: a way for dealing with uncertainty in CBIR systems, University of Naples Parthenope, Department of Applied Science; Flickner M., Sawhney H., Niblack W., Ashley J., Huang Q., Dom B., Gorkani M., Hafher J., Lee D., Petkovie D., Steele D. and Yanker P.(1995) Query by Image and Video Content: The QBIC System, IBM Almaden Research Center; available at: http://www2.cs.ucy.ac.cy/~nicolast/courses/cs422/ReadingProjects/qbic.pdf last accessed on 24th September 2009; Hafiane A., Chaudhuri S., Seetharaman G., Zavidovique B. (2006) Region-based CBIR in GIS with local space filling curves to spatial representation Huang J., Kumar S. R., Mitra M., Zhu W. J., Zabih R. (1997) Image Indexing Using Color Correlograms, Cornell University; Lowe D. G. (2004), Distinctive Image Features from Scale-Invariant Keypoints, Computer Science Department University of British Columbia; available at: http://people.cs.ubc.ca/~lowe/papers/ijcv04.pdf last accessed on 24th September 2009 Nixon M. S., Aguado A. S. (2007) Feature Extraction and Image Processing, Academic Press; Suhasini P.S., Dr. K. Sri Rama Krishna, Dr. I. V. Murali Krishna (2009) CBIR Using Color Histogram Processing; VR Siddhartha Engineering College; available at: http://www.jatit.org/volumes/research-papers/Vol6No1/13Vol6No1.pdf last accessed on 24th September 2009; Tahmoush D.   Hanan S. (2007)A Web Collaboration System for Content-Based Image Retrieval of Medical imag;available at:http://www.cs.umd.edu/~hjs/pubs/medicalimagepapers/TahmoushSPIE07a.pdf last accessed on 24th September 2009; Torres R. S., Falcà £o A. X. (2006)Content-Based Image Retrieval: Theory and Applications; available at: http://www.dcc.unicamp.br/~rtorres/artigos/journal/torres06rita.pdf last accessed on 24th September 2009; Veltkamp R. C., Tanase M. (2002) Content-Based Image Retrieval Systems: A Survey; Department of Computing Science, Utrecht University; available at: http://give-lab.cs.uu.nl/cbirsurvey/cbir-survey.pdf last accessed on 24th September 2009; Wang J. Z. (2001) SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture Libraries; available at: http://infolab.stanford.edu/~wangz/project/imsearch/SIMPLIcity/TPAMI/wang2.pdf last accessed on 24th September 2009; Wen Ch. Y, Yu Ch. Y., (2005) Image Retrieval of Digital Crime Scene Images, Forensic Science Journal; available at: http://fsjournal.cpu.edu.tw/content/vol4.no.1/06-95-04.pdf last accessed on 24th September 2009.

demand Essay -- essays research papers

Firstly, I will deal with the factors which can affect the demand for houses in an economy.In many people’s opinion, the single most important factor which affects demand for housing is interest rates. This belief is held because for most people, the cost of purchasing a house is so great that the only way they can afford to do so is to take out a mortgage from a bank or building society. One of the main conditions that banks and building societies apply to mortgages is that during the course of the mortgage, interest will be paid on the loan. Although it is possible to have a fixed rate mortgage - where the rate of interest which will be paid is fixed at a constant level throughout the mortgage- most mortgages are variable rate mortgages, where the amount of interest which will be paid varies throughout the mortgage [1]. By increasing interest rates, the government can control how much money people have in their pockets. The variance of interest rate can be used to control m uch of the economy, including inflation. This is known as monetary fiscal policy. Interest rates have such a large affect on the economy because such a large percentage of the population has a mortgage and so is vulnerable to interest rate rises. An increase in interest rates can greatly increase the amount of money that a household has to pay each month. If people without a mortgage who are considering taking one out to cover the cost of a very expensive purchase see that interest rates are high then they are likely to be wary of taking out a mortgage, as they know that they will have to pay a greater amount of extra money each month. Because people may be put off taking out mortgages, they will be unable to purchase a house, so this will cause demand for houses to fall. This is known as a slump in the housing market. Conversely, if people see that interest rates are low and they are considering the possibility of purchasing a house, they may decide to go ahead with their purchase due to the fact that it will be more affordable- at least in the short run- due to the lower interest rates. Variable rates will also make mortgagors vulnerable to fluctuations in interest rates as even small changes in the interest rate can have a big effect on the outgoings of those with large mortgages. When rates rise steeply, one likely result is an increase in the number of mortgagors who cannot afford ... ... of their home being repossessed increases. If a large number of homes are being repossessed, people will be put-off purchasing a house. Also, people who are considering taking out a mortgage will be less likely to do so because they will be less able to afford it due to the fact that they are paying more tax. Both of these effects are likely to cause a slump in the housing market. Conversely, if tax rates are low the state of the housing market will improve due to the fact that people will feel that they are more able to afford the added cost of a mortgage. However, if they take out a mortgage at a time when tax rates are low, there is always the possibility that tax rates will increase at a later date. [1]The interest rate is set by the lender, but it is usually in line with the rate set by the Bank Of England, which is turn is likely to respond to changes in the interest rates of the main world banks, most of which are in America. [2]This is because there is a large labour pool from which to draw replacement workers. However, there are exceptions to this rule, for example people in the professions like doctors and lawyers tend to have a high degree of job security at all times.

Central Dogma

The Central Dogma of Molecular Biology was founded by Francis Crick in 1958. A central dogma of biology provides an explanation as to how gene expression occurs. The central dogma is the main thesis of molecular inheritance. It states that DNA makes RNA, which makes protein. Genes control the traits by controlling which proteins are made. The process of Central Dogma of Molecular Biology is when DNA transcripts into RNA and then translates into protein. Transcription is the transfer of genetic information from DNA forming into RNA.The differences between DNA and RNA are the sugar that’s in DNA which is called deoxyribose and ribose for RNA which does not have sugar. When DNA replication begins, it begins at a specific point in the DNA molecule called the origin of replication site. The enzyme helicase unwinds and separates a portion of the DNA molecule. After the DNA polymerase separates a portion of the molecule it then initiates the process of replication in which DNA polyme rase can add new nucleotides to a pre-existing chain of nucleotides.Therefore, replication begins as an enzyme called primase and it assembles an RNA primer at the origin of the replication site. The RNA primer consists of a sequence of RNA nucleotides, complementary to a section of the DNA strand that is being prepared for replication. The RNA primer is then removed and replaced with a sequence of DNA nucleotides. Then Okazaki fragments are synthesized and the RNA primers are replaced with DNA nucleotides and the individual Okzaki fragments are bonded together into a continuous complementary strand.During transcription deoxyribose nucleic acid is formed into another nucleic acid which is ribonucleic acid or RNA. Transcription begins when RNA polymerase binds onto the double stranded DNA molecule. RNA polymerase moves along the strand of DNA making a complementary single stranded RNA molecule. Here’s a good thing you could remember, take the root word ‘scribe’ ou t of transcription and think of it was a person who writes copies of important documents because that is what scribe means.Next is translation, it is the process of using the code in RNA to put together the protein and translation is a word that describes the transfer of information from one to another. Translation begins when messenger RNA binds to the ribosome. The RNA passes along the ribosome and brings out 3 nucleotides at a time. While that’s happening the amino acid that is being carried is also being transferred to the amino acid chain. After that is done the ribosomal complex falls apart and the protein is released into a cell.During protein synthesis, amino acids build a protein molecule that’s, of course, called protein synthesis. Synthesis means ‘putting together’, so that is a good way to remember protein synthesis. Protein synthesis is the cellular process of building proteins. Translation has a part of the central dogma that is also included in protein synthesis and transcription is not. Translation is just the decoding of RNA to make a chain of amino acids that will then, later, turn into protein. Overall in central dogma, DNA is simply the instructions to making proteins.

Ethics: Utilitarianism Essay

The theory behind utilitarianism is that one’s actions are right if it promotes happiness or pleasure and wrong if it does not promote happiness or pleasure. The main point to this theory is the principle of utility that states â€Å"according to which actions should be chosen that bring about the greatest amount of happiness for the greatest number of people. † (Palmer) Jeremy Bentham gave essentially utilitarianism its name and brought more attention to it than those before him. Bentham came up with a guide named the calculus of felicity that included seven categories for choosing among different possible activities to promote one’s happiness or pleasures. John Stuart Mills, also an utilitarian, added to Bentham’s calculus because he did not fully agree with everything it stood for. Bernard Williams argued that utilitarianism is not a good moral theory and that it violates moral integrity. In this paper I will explain Bentham’s calculus of felicity, Mill’s addition to the calculus, and Williams’ thoughts against utilitarianism. The first category of the calculus of felicity is intensity which asked how intense are the pleasures likely to be. The second is duration. Duration refers to the question of how long the pleasures are to last. The third is certainty. The question of certainty is how certain are the pleasures. The fourth is propinquity which refers to how soon will the pleasures be available. Number five on the calculus is fecundity. Fecundity wants one to think about how many more pleasures will follow in their wake. Number six is purity. Purity wants one to question how free from pain are the pleasures. Lastly, number seven is extent. Extent questions how many people will receive pleasure and be affected by your acts. Mills understood Bentham’s theory to be quantitative with a numerical analysis. Mills preferred to think of utilitarianism as a qualitative analysis. Mills believed in different types of pleasures. Pleasures of the body and pleasures of the mind. Mills considered the quantitative analysis to fulfill the â€Å"lower† desires, or the basic human desires. The â€Å"higher† desires are the ones dealing with quality. Mills’ theory suggests that the lower quality pleasures are those of the body and the pleasures of the mind are the higher quality pleasures. Mills thought higher of the pleasures of intellect than that of pleasing our bodies. An example of this is giving someone a choice of having the price of beer reduced or continue paying to keep teachings of Shakespeare in schools. I believe Mills’ concern was that some people would most likely choose taking a price reduction in beer over Shakespeare. This would not be Mills’ choice and he would think this as humans satisfying his or her lower quality pleasures. Williams believes that utilitarianism decisions are not based on any kind of moral ground and looks out for one’s self interest only. Williams believes in a deeper meaning to things based on morals for not only oneself but of others also. Williams gives a story in his writing of a man named George. George has been offered a job in a laboratory in which the research is in chemical and biological warfare. George refuses the job because he is opposed to biological nd chemical warfare. The man offering the job doesn’t understand George’s decision because George has a wife and kids at home to support. The utilitarianist would agree that George should take the job. I believe Williams’ problem with this is the bigger picture that George is opposed to what the company stands for and the research he would be doing could ultimately affect a whole country. As Williams states in his writing, â€Å"A feature of utilitarianism is that it cuts out a kind of consideration which for some others makes a difference to what they feel about such cases: a consideration involving the idea, as we might first and very simply put it, that each of us is specially responsible for what he does, rather than for what other people do. (Williams) Integrity is compromised in the utilitarianism choice because it does not stand on a deep moral ideal. The choice to take the job would destroy George’s integrity by holding him responsible for something that he does not do about his opposing feelings and threatening the idea of his boundaries. I can appreciate the views of Bentham, Mills and Williams theories, but I personally agree with Williams’ concepts the most. I agree with Williams that we as humans should take the whole situation, who it involves currently, and who it will potentially affect in the future. We should not just seek self pleasure. To dissect how much pleasure, for how long, and so on does not take into consideration the factor of long term effects. As for Mills’ conclusion that intellectual pleasure is best, that is just his opinion on expanding the mind. It does not give answers to everyday dilemmas.