Author: Nikolai
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Entropy in Thermodynamics with Real-World Applications
There are many interpretations of entropy as used in physics: measure of disorder, how spread out the energy is, measure of unavailability of energy to do work, etc. All of these interpretations put certain properties on the concept of energy and heat transfer. As most interpretations of entropy rely heavily on thermodynamic processes, this article Read more
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Systems of Two Shafts: Rotational Mechanics
Shafts are used everywhere mechanical power needs to be transmitted from one location to another. Shaft rotational mechanics appear in machines, vehicles, tools, appliances, and industrial systems. Below is a clear breakdown. It is important to understand how different forces affect shaft rotational mechanics. This article primarily focuses on shafts used in turbines and briefly Read more
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Backpropagation: Fundamentals of Neural Networks Part 3
Modern neural network architectures are deep – typically involving 3 and more hidden layers. They use backpropagation to determine the rate of weight updates through all layers. Backpropagation was first popularized by the paper Learning representations by back-propagating errors | Nature back in 1986. It is one of the most important algorithms in machine learning. Read more
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Estimating Camera Parameters Using Tsai’s Method: A Practical Experiment
Abstract This report outlines a practical implementation of the Tsai camera calibration technique, excluding the effects of radial distortion. It explains the calibration procedure, presents the results, evaluates calibration accuracy using reprojection errors, and concludes with suggestions for improvement and future experimentation. Keywords: Calibration 1. Introduction Camera calibration is a fundamental task in computer vision, Read more
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AI and Robotics Replications of Human Capabilities: Part 1
Humanity has always been looking towards replicating human capabilities through the use of machines, mechanisms, technologies, computers and computer programs. This first resulted in a shift from human labor to mechanization and automation by technologies and machines. Nowadays, the trend continues in the form of robotization: AI and Robotics. Robotics vs AI We use our Read more
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The Physics of Cycling: Understanding Bicycle Motion and Stability
Bicycles are a fundamental mode of transportation, recreation, and sport, yet the physics behind their motion is often overlooked. Understanding the forces at play when riding a bicycle reveals the intricate balance, stability, and mechanics that make cycling possible. This document explores the fundamental principles behind the physics of cycling. This includes bicycle motion, the Read more
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Foundations of Game Grid Designs: The Only 3 Regular Grids That Work
Most strategy games like chess, civilization 4 and 5, generals.io and others use a game grid design. It defines a map grid that you use to place units, buildings, etc. The grid has to consist of regular polygons to cover the entire map and leave no blank spaces. The question is, how many regular polygons Read more
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Sigmoid Neuron – Fundamentals of Neural Networks Part 2
Introduction to Sigmoid Function Previously we have covered Single-Node Perceptron neural networks. The next important topics to study are the sigmoid neuron of a neural network and logistic regression. Sigmoid function is defined as: This function originates in mathematical biology. Various fields have used it long before its adoption in artificial neural networks. Here’s a Read more
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From Cameras to 3D: The Magic of Stereo Vision in Action
Humans and other animals have long been capable of detecting depth in three dimensions with the help of binocular vision. The goal of computer stereo vision is to give a machine the same capabilities. It makes it possible to rebuild a three-dimensional depiction of the environment. In keeping with that, this study will first give Read more
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Single-Node Perceptron: Fundamentals of Neural Networks Part 1
Introduction Neural networks have been part of AI research for quite some time. It all started with a perceptron, which is a simplest Artificial Neural Network. It was introduced in 1958 by Frank Rosenblatt. Despite its simplicity, it serves as a fundamental building block in artificial neural networks and has several practical applications. It excels Read more