Found 47 Articles for Scientific Computing

The use of Internet of Things (IoT) devices conveys huge support to both businesses & consumers. However, this rapid growth is putting a lot of stress on traditional data center infrastructure. To address this, edge computing is evolving more prevalent. As it enables processing power to be closer to the devices that generate or consume data. This article will provide an introduction to the fundamentals of IoT, Cloud & edge computing & discuss the key reasons why they are a good fit. What is Edge Computing? Edge computing is a method of decentralizing IoT devices by ... Read More

Edge computing & 5G are two of the most essential technologies & the mixture of these two technologies has a high potential to transform the way IoT devices work, causing them more efficient, faster, & more reliable. In this article, we will examine how edge computing and 5G will help IoT. What is Edge Computing? A distributed computing paradigm known as edge computing enables data processing and storage to be carried out near the data sources, such as IoT devices, sensors, and mobile devices. This strategy aims to minimize latency, decrease bandwidth usage, & enhance overall performance & ... Read More

In scientific research, errors can occur during the measurement of data that can affect the accuracy and reliability of the results. These errors can be classified into two categories: systematic error and random error. While both types of errors can affect the accuracy of research findings, they differ in terms of their nature, causes, and consequences. This essay aims to provide a detailed explanation of the difference between systematic error and random error. What is Systematic Error? Systematic errors are caused by flaws in the measurement process that consistently bias the results in a particular direction. These errors are often ... Read More

What are the uses of gauss pulse? Gaussian pulses are widely used in signal processing, particularly in radar, sonar, and communications. This pulse is a pulse that has a Gaussian shape in the time domain, which makes it useful for detecting small signals that may be obscured by noise. In this tutorial, we will explore how to generate a Gaussian pulse using the scipy.signal.gausspulse function. What is a Gaussian Pulse? Gaussian pulses are a type of function that has a Gaussian-shaped envelope in the time domain. The Gaussian function is a bell-shaped curve that is symmetrical around its peak. It ... Read More

The sqrtm() function of scipy.linalg package can be used to find the square root of an input matrix.Syntaxscipy.linalg.sqrtm(x)Example 1Let us consider the following example −# Importing the required libraries from scipy from scipy import linalg import numpy as np # Define the input array x = np.array([[14 , 2] , [89 , 33]]) print("Input array:", x) # Calculate the square root r = linalg.sqrtm(x) # Display the square root print("Square Root of x: ", r)OutputIt will generate the following output −Input array: [[14 2] [89 33]] Square Root of x: [[3.43430132 0.22262855] [9.90697038 5.54927253]]Example 2Let us take ... Read More

The norm() function of the scipy.linalg package is used to return one of eight different matrix norms or one of an infinite number of vector norms.Syntaxscipy.linalg.norm(x)Where x is an input array or a square matrix.Example 1Let us consider the following example −# Importing the required libraries from scipy from scipy import linalg import numpy as np # Define the input array x = np.array([7 , 4]) print("Input array:", x) # Calculate the L2 norm r = linalg.norm(x) # Calculate the L1 norm s = linalg.norm(x, 3) # Display the norm values print("Norm Value of r :", ... Read More

The scipy.linalg package contains a of different functionalities that are used for Linear Algebra. One of them is the inv() function, which is used to find the inverse of a square matrix.Syntaxscipy.linalg.inv(x)Where x is a square matrix.Example 1Let us consider the following example −# Import the required libraries from scipy import linalg import numpy as np # defines the array a = np.array([[5, 3], [6, 4]]) print("Input matrix :", a) # Finding the inverse of a square matrix x = linalg.inv(a) print(" Inverse of Square Matrix A :", x)OutputThe above program will generate the following output −Input matrix ... Read More

The scipy.linalg package contains a set of different functionalities that are used for Linear Algebra. One of them is the det() function. This function is used to find the determinant of a two-dimensional matrix.Syntaxscipy.linalg.det(x)Where x is a square matrix.Example 1Let us consider the following example −# Importing the required libraries from scipy import linalg import numpy as np # Initialize the matrix A A = np.array([[8, 5], [3, 4]]) print("Input Matrix :", A) # Find the determinant of matrix X x = linalg.det(A) print("Determinant Value of A:", x)OutputIt will generate the following output −Input Matrix : [[8 5] ... Read More

The scipy.special package contains a set of different functionalities that are used for mathematical physics. One of them is the logsumexp() function. This function is used to compute the log of the sum of exponentials of input elements. Let us take a couple of examples and see how to use this function.Syntaxscipy.special.logsumexp(x)where, x is the input value.Example 1Let us consider the following example −# Import logsumexp from scipy.special from scipy.special import logsumexp import numpy as np # Input array a = np.arange(10) print("Input Array:", a) # logsum() function res = logsumexp(a) print("logsumexp of a:", res)OutputIt will produce the ... Read More

The linear function named scipy.linalg.solve_toeplitz is used to solve the Toeplitz matrix equation. The form of this function is as follows −scipy.linalg.solve_toeplitz(c_or_cr, b, check_finite=True)This linear function will solve the equation Tx = b for x where T is the Toeplitz matrix.ParametersBelow are given the parameters of the function scipy.linalg.solve_toeplitz()c_or_cr− array_like or tuple of (array_like, array_like)This parameter is the vector c or tuple of arrays (c, r). Despite the actual shape of c, it will always be converted to a one-dimensional array. If r is not given, the assumption made is r = conjugate(c). Below are given two cases −    ... Read More