According to the convolution theorem for Fourier transform, the convolution of two signals in the time domain is equivalent to the multiplication in the frequency domain. Therefore, if two signals are convolved in the time domain, they result the same if their Fourier transforms are multiplied in the frequency domain. For example, if x(t) and h(t) are two signals in the time domain and their Fourier transforms are X(ω) and H(ω) respectively. Then, their convolution in the time domain is given by, f(t) = x(t) * h(t) Here, the symbol ‘*’ represent the convolution of two signals. And the ... Read More

In digital signal processing and image processing, the convolution is a crucial mathematical operation. The primary function of the convolution is to combine two signals to produce a resulting signal. The resulting signal specifies the interaction between the two signals that we combined. In MATLAB, we can perform the convolution of two signals by using a built−in function ‘conv()’. This MATLAB function performs the convolution by using a discrete approximation of the integration. For this, it uses discrete samples of the input signals. Syntax The following is the general syntax for the ‘conv()’ function, a = conv(x, y, shape); ... Read More

Introduction Data mining is a powerful technique used to extract meaningful insights and patterns from large datasets. It involves the application of statistical and computational algorithms to uncover hidden relationships and trends within the data. One popular tool for data mining is the programming language R. In this article, we will delve into the world of data mining with R, exploring its capabilities and applications. Understanding Data Mining Data mining is the process of discovering patterns, relationships, and insights from large datasets. It involves several steps, including data preprocessing, exploratory data analysis, model building, and evaluation. Data mining techniques can ... Read More

The NumPy library has a wide range of tools which perform the comparison and filtering of the arrays. The comparison of arrays will be done element by element in row wise and column wise based on the dimension of the array. When we want to retrieve a particular array element, then we can apply the filtering. NumPy is abbreviated as Numerical python, which is used to perform mathematical and scientific calculations on multidimensional arrays and matrices. There are different functions and methods available in NumPy to perform the filtering and comparison of the arrays. Comparing NumPy Arrays Following are ... Read More

Introduction Big data analytics has become an integral part of decision-making and business intelligence across various industries. With the exponential growth of data, organizations need robust tools and techniques to extract meaningful insights. R, a powerful programming language and software environment, has gained popularity for its extensive capabilities in data analysis and statistical computing. In this comprehensive guide, we will explore how R can be effectively utilized for big data analytics, covering various aspects and techniques. Understanding R for Big Data Analytics R Programming Language: R is an open-source programming language that provides a wide range of statistical and graphical ... Read More

Introduction R, a powerful programming language, offers a wide range of packages specifically designed for financial analysis and modeling. These packages provide robust tools and functions to handle various aspects of finance, including data manipulation, statistical analysis, portfolio management, risk assessment, and visualization. In this article, we will explore some of the popular R packages for finance and delve into their key features and applications. Data Manipulation Packages dplyr − dplyr is a versatile package that simplifies data manipulation tasks in R. It provides a concise grammar for data manipulation, allowing users to easily filter, arrange, select, mutate, and summarize ... Read More

Introduction Machine learning has revolutionized the field of artificial intelligence and data analysis. With the ever-increasing availability of data and computational power, machine learning algorithms have become essential tools for extracting valuable insights and making predictions from large and complex datasets. Among the various programming languages used in machine learning, R stands out as a popular choice due to its versatility and extensive library support. In this article, we will provide a comprehensive introduction to R for machine learning, exploring its capabilities, libraries, and applications. What is R? R is a powerful statistical programming language widely used for data analysis, ... Read More

In this article, we will learn to configure the Run button for functions in MATLAB. In MATLAB, the `Run` button is used execute a MATLAB code. Also, MATLAB allows us to configure the `Run` button to execute a specific user defined function. Algorithm to Configure Run Button The step−by−step process to configure the Run button to execute a specific function in MATLAB is explained below: Step 1 − Firstly, create a new script to open the MATLAB editor. Step 2 − Define or create a function for which you want to configure the Run button. For example, we create ... Read More

Introduction Data visualization plays a crucial role in analyzing and communicating insights from complex datasets. With the advent of advanced data visualization tools and techniques, researchers and analysts can now present data in more meaningful and engaging ways. In this article, we will explore advanced data visualization techniques using R, a powerful programming language widely used for data analysis and visualization. We will delve into various subheadings that cover different aspects of data visualization in R, providing detailed insights and examples. Exploratory Data Analysis (EDA) Visualizations Exploratory Data Analysis (EDA) involves visualizing and understanding the underlying patterns and relationships in ... Read More

In this article, we will learn how to compare two cell arrays of strings of different sizes in MATLAB. Cell Array of Strings In MATLAB, a cell array of strings is a data structure that can store different types of elements. The element of a cell array are enclosed within curly braces `{}`. Syntax We can use the following syntax to create a cell array of strings, A = {'string1', 'string2', 'string3', …'stringN'}; Here, `A` is a cell array of strings that contains N string elements namely, `string1`, `string2`, …, `stringN`. In MATLAB, the cell arrays of strings are ... Read More