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This project work also aims at determining the correct value of density by clearing the objects touching the borders of the image. In this project three applications are taken into account and using Matlab with image processing toolbox the count and density values are calculated for each.
MATLAB (an abbreviation of "MATrix LABoratory" [18]) is a proprietary multi-paradigm programming language and numeric computing environment developed by MathWorks.MATLAB allows matrix manipulations, plotting of functions and data, implementation of algorithms, creation of user interfaces, and interfacing with programs written in other languages.
Kernel density estimation of 100 normally distributed random numbers using different smoothing bandwidths.. In statistics, kernel density estimation (KDE) is the application of kernel smoothing for probability density estimation, i.e., a non-parametric method to estimate the probability density function of a random variable based on kernels as weights.
MATLAB was created in the 1970s by Cleve Moler, who was chairman of the computer science department at the University of New Mexico at the time. It was a free tool for academics. Jack Little, who would eventually set up the company, came across the tool while he was a graduate student in electrical engineering at Stanford University.
Ways to study a system. The term computational scientist is used to describe someone skilled in scientific computing. Such a person is usually a scientist, an engineer, or an applied mathematician who applies high-performance computing in different ways to advance the state-of-the-art in their respective applied disciplines in physics, chemistry, or engineering.
GNU Octave is a scientific programming language for scientific computing and numerical computation.Octave helps in solving linear and nonlinear problems numerically, and for performing other numerical experiments using a language that is mostly compatible with MATLAB.
The primary application of the Levenberg–Marquardt algorithm is in the least-squares curve fitting problem: given a set of empirical pairs (,) of independent and dependent variables, find the parameters of the model curve (,) so that the sum of the squares of the deviations () is minimized:
In MATLAB we can use Empirical cumulative distribution function (cdf) plot; jmp from SAS, the CDF plot creates a plot of the empirical cumulative distribution function. Minitab, create an Empirical CDF; Mathwave, we can fit probability distribution to our data; Dataplot, we can plot Empirical CDF plot; Scipy, we can use scipy.stats.ecdf