The assignment is writing a paper 13-pages long that present a recent paper in a given topic (applying CA in image processing) in my own words. I have written, however I need a thorough revision for the content, structure, flow of writing, and correctness of writing
Take-Home Assignment – 1 (40%) Description of self-study material Final Take-Home Assignment Description Topic: “CA Model of XX and its behaviour”, or “CA Modeling of XX and their behaviour”. Content: in your article, you need to • describe the application domain of the problem, i.e. what type of system or problem is being modelled and studied by CA • provide a short literature review • motivate why CA was/were selected as modelling methodology • describe the CA model (models) you selected • discuss the obtained results (i.e., the behaviour of the model) and what was learned from the research using the selected model (models) • discuss how you would improve the presented research (i.e., the model and/or its analysis) • provide a full list of references to the works you have used in the preparation of your article. If possible, include the links to these references. Remember to cite properly in text. • Write, an approximately 13-page long article, but not longer than 15 pages on “CA Model of XX and its behaviour”, or “CA Modeling of XX and their behaviour”. XX stands for what you select. • The page count includes the figures and the list of references. The page count does not include a separate title page if you choose to include one. • You are free to choose the topic, i.e. the type of CA model or models you want to describe, except for modelling the spread of epidemics and vaccination strategies (i.e., these topics are excluded). For example, you can write a paper on CA modelling of growth of biofilm, or spread of cancer, or vehicular traffic, or CA applications to image processing, and so on. • For your modelling project, you may use some of the references as a starting point that I provided below or you can find your own main paper that you want to present in your write-up. The paper must be a refereed journal or conference proceedings paper and you must provide a full reference to this paper including the link to the paper, if possible. However, your write-up cannot be based solely on one reference. It must provide a short review of the relevant literature. Usually, such a literature review is provided in the paper. • It is advisable that your write-up is based on recent literature. The main paper of your write-up should not be published prior to 2000. The references in your short review can be older. • When you write the paper, please, pay attention to the structure of your paper. Your paper should start with an introduction in which you tell a reader clearly what is the paper about and how it is organized. Your paper must include a short literature review and conclusions. Also, properly organize the material with relevant titles of the sections. • You must write the assignment in your own words, properly quoting, citing and providing full references with the links to the papers, if possible. Citation numbers must appear next to the text they are referring to. This is called an in-text citation. • Your assignment will be automatically checked for similarity score to detect possible plagiarism. If you are not sure what plagiarism means and how to avoid it, please, review the material of the first week of classes. • You must type your article in 12 pt and use double space. You may include figures to make your point and the presentation clear. Each figure must have a figure caption and proper citations and references. • If you have problems with the literature search or writing, please, review the material of the first week of classes and contact the library or writing services for help. Below there is a list with various references to CA modelling. You may use these references or you may find your own references. References: textbook posted on the Internet • Stephen Wolfram, A New Kind of Science http://www.wolframscience.com/ http://www.wolframscience.com/nksonline/toc.html • N. Boccara, Modeling Complex Systems, second edition http://www.fulviofrisone.com/attachments/article/412/modeling%20complex%20systems%20- %20boccara.pdf • Emerging Applications of Cellular Automata-Book, edited by A. Salcido http://www.intechopen.com/books/emerging-applications-of-cellular-automata • Daniel Shiffman, The Nature of Code http://natureofcode.com/book/ • Hiroki Sayama, Introduction to the Modeling and Analysis of Complex Systems http://polymer.bu.edu/hes/book-sayama.pdf Some books are Online in the UoG Library • A. Deutsch and S. Dormann, Cellular Automaton Modeling of Biological Pattern Formation https://oculgue. primo.exlibrisgroup.com/discovery/fulldisplay?docid=alma9953168863605154&context =L&vid=01OCUL_GUE:GUELPH&lang=en&search_scope=OCULDiscoveryNetwork&ada ptor=Local%20Search%20Engine&tab=OCULDiscoveryNetwork&query=any,contains,intro duction%20to%20cellular%20automata&offset=0 • Cellular Automata: A Discrete View of the World by Joel L. Schiff, 2007, available online at the UoG Library https://onlinelibrary-wiley-com.subzero.lib.uoguelph.ca/doi/book/10.1002/9781118032381 • Lowndes V., Bird A., Berry S. (2017) Introduction to Cellular Automata in Simulation. In: Berry S., Lowndes V., Trovati M. (eds) Guide to Computational Modelling for Decision Processes. Simulation Foundations, Methods and Applications. Springer, Cham. https://doi-org.subzero.lib.uoguelph.ca/10.1007/978-3-319-55417-4_2 Hard copies of books available at the UoG Library • M. Battay, Cities and Complexity, Understanding Cities with Cellular Automta, Agent-Based Models, and Fractals https://mitpress.mit.edu/books/cities-and-complexity http://www.complexcity.info/ http://www.complexcity.info/files/2011/12/BATTY-CITIES-2011.pdf • Cellular Automata Modeling of Physical Systems, by Bastien Chopard and Michel Droz (hard copy available from the library) https://www.researchgate.net/profile/Michel- Droz/publication/216300438_Cellular_Automata_Modeling_of_Physical_Systems/links/00b 49526fb2ee12d01000000/Cellular-Automata-Modeling-of-Physical-Systems.pdf Articles • You can find many articles in the Proceedings of the International Conference on Cellular Automata for Research and Industry (ACRI) published in LNCS by Springer. Many volumes of the proceedings are available Online in the library. • You can find many additional articles on CA modelling by searching https://arxiv.org/ or Google Scholar https://scholar.google.ca/scholar?hl=en&q=cellular+automata&as_sdt=1%2C5&as_sdtp=&o q=cellular+ • Below I am providing links to various articles. Full text of these articles you may find via the UoG Library • Cellular Automata and It’s Applications in Bioinformatics A Review, Pokkuluri Kiran Sree, Inampudi Ramesh Babu, SSSN Usha Devi .N https://arxiv.org/ftp/arxiv/papers/1404/1404.0453.pdf • Cellular Automata in Image Processing https://pdfs.semanticscholar.org/56f4/4a60caa2687186ef18fd0dc9a7a531c174ae.pdf https://arxiv.org/ftp/arxiv/papers/1407/1407.7626.pdf http://www.sciencedirect.com/science/article/pii/S1077314210000652 http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=5BFB89BA1E5367BB13F9888647D7 1168?doi=10.1.1.617.9788&rep=rep1&type=pdf http://www.intechopen.com/books/cellular-automata-innovative-modelling-for-science-andengineering/ cellular-automata-for-medical-image-processing http://ieeexplore.ieee.org/document/4379241/ http://link.springer.com/chapter/10.1007%2F978-3-319-06431-4_9#page-1 • Cellular Automata in Medicine (several articles are about CA cancer modeling) http://www.sciencedirect.com/science/article/pii/S1877042811025900 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3611882/ https://www.researchgate.net/publication/228852572_A_Review_of_Cellular_Automata_Models _of_Tumor_Growth https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1002314 https://ascopubs.org/doi/full/10.1200/CCI.18.00069?fbclid=IwAR2rd5fPdtiKe42GJTK7bw6TH1 BRCjEf4hf3dZzInbIf9nW-IKoiJKc2Pro https://www.worldscientific.com/doi/abs/10.1142/S0219525902000572 https://www.sciencedirect.com/science/article/pii/S2352914817302435 https://www.tandfonline.com/doi/full/10.1080/13873954.2019.1571515 https://link.springer.com/chapter/10.1007/978-3-030-15715-9_8 https://pubmed.ncbi.nlm.nih.gov/8501923/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4208695/ https://ysjournal.com/a-3d-cellular-automata-cancer-stem-cell-model-using-matlab-and-appdesigner/ • Cellular Automata in Biology https://www.ncbi.nlm.nih.gov/pubmed/8474249 http://www.sciencedirect.com/science/article/pii/089571779090010K https://en.wikibooks.org/wiki/Cellular_Automata/Applications_of_Cellular_Automata http://www.springer.com/us/book/9780817642815 http://schulmanlab.jhu.edu/papers/reaction-diffusion-ca-journal-version.pdf https://www.researchgate.net/publication/345252633_BIOLGCA_ a_cellular_automaton_modelling_class_for_analysing_collective_cell_migration • Cellular Automata in Data Communication Networks https://pdfs CA Model of Edge Detection and its behavior 1. Application Description Image processing is a method of transforming an image into a digitized form and performing operations on it to enhance the image quality and extract useful information. Whether digital image processing is referred to as image processing, three factors have influenced the generation and development of digital image processing: the advancement of computers; the advancement of mathematics; and the increased demand for a wide variety of applications in various industries. Computer vision, remote sensing, feature extraction, face detection, forecasting, optical character recognition, finger-print detection, optical sorting, argument reality, microscope imaging, and medical image processing are some of the valuable image processing applications in science and technology fields [1]. Edge detection is one of the fundamental steps in image processing. Edge detection is an approach that aims to capture the important properties of objects in the picture. Discontinuities in an object's photometrical, geometrical, and physical qualities are examples of such properties [2]. The physical edges are related to dramatic changes in a scene's reflectance, illumination, orientation, and depth. Physical edges are represented in the picture by variations in the intensity function, which is frequently proportional to scene radiance. Thus, steps, lines, and junctions are the most common types of image intensity variations [2]. The performance of a system is directly influenced by the high levels of accuracy and reliability of edge detection techniques’ outputs. However, creating an efficient and robust edge detector algorithm is a complicated process [3]. The aim is to obtain an edge detection approach that implements effectively in a wide range of situations and conditions while still meeting the objectives of subsequent processing steps. So, various edge detectors have been developed throughout the history of digital image processing, each with its own set of characteristics and algorithmic and mathematical attributes [4]. While each edge detection method has its merits and shortcomings, more research should be implemented with considering substantial investigations: correctness and accuracy of edge detection; enhancing the capacity of avoiding the noise; improving the detection sensitivity; and reducing the ratio of missed detection [5]. In the context of developing other techniques to increase the anticipated result's quality as well as minimize processing time, many researchers have studied the ability of cellular automata (CA) to manage those challenges[5]. They determined that CA exceeds traditional methods, thus, recently, CA has been of interest to experts to perform edge detections [5]. Cellular Automata (CA) are one of the oldest natural computing models. Cellular Automata (CA) was initially suggested by Ulam [6] and Von Neumann [7] with the goal of creating models of biological self-reproduction in the early 1950s. Following that, Stephen Wolfram [8] developed the CA theory [9]. Cellular Automata is a basic parallel computation paradigm in which a cell changes its state depending on the states of its neighbors and its own, according to a certain transition rule [10]. The main advantage of CA is that each cell follows a few fundamental rules by default; but, when a matrix of cells is joined with corresponding local interactions, more complex evolving global behavior emerges [5]. The utilization of CA in edge detection has several benefits, including ease of processing, parallel implementation, and the ability to be performed on images of various kinds (binary, grey, and color) and sizes (2D or 3D) [10]. The prominence of cellular automata can be linked to their simplicity as well as their enormous potential for depicting complex systems despite their inherent parallelism [11]. It has been proven to be effective in a variety of image processing tasks, including grid structure, local interactions, developing behavior, and short processing time [5]. Various scientists have proposed a variety of CA models to develop the design and modeling of complex systems such as edge detection tasks in image processing systems. Thus, a range of CA models used for image processing, particularly edge detection, will be presented in the linked literature. In this write-up, a new edge detection algorithm using Outer Totalistic Cellular Automata proposed recently by Djemame and Fichouche [5], will be analyzed. Section 2 describes the related works done by different authors. Section 3 describes the concept of CA and why it has been selected. Section 4 describes the OTCA