Rolling Windows in Signal Processing
Explore the diverse applications of rolling windows in signal processing, covering both the underlying theory and practical implementations.
Recent posts
Exploring the Dynamics of Traffic Control and Pedestrian Behavior Through the Lens of Fluid Dynamics
This article explores the complex interplay between traffic control, pedestrian movement, and the application of fluid dynamics to model and manage these phenomena in urban environments.
The Fears Surrounding Artificial Intelligence
Delve into the fears and complexities of artificial intelligence and automation, addressing concerns like job displacement, data privacy, ethical decision-making, and the true capabilities and limitations of AI.
Binary Classification: Explained
Learn the core concepts of binary classification, explore common algorithms like Decision Trees and SVMs, and discover how to evaluate performance using precision, recall, and F1-score.
Understanding the Difference Between Regression and Path Analysis
Regression and path analysis are two statistical techniques used to model relationships between variables. This article explains their differences, highlighting key features and use cases for each.
Ethics in Data Science
A deep dive into the ethical challenges of data science, covering privacy, bias, social impact, and the need for responsible AI decision-making.
Applying R Functions on Rolling Windows Using the runner Package
Explore the runner package in R, which allows applying any R function to rolling windows of data with full control over window size, lags, and index types.
Multivariate Analysis of Variance (MANOVA) vs. ANOVA: When to Analyze Multiple Dependent Variables
Learn the key differences between MANOVA and ANOVA, and when to apply them in experimental designs with multiple dependent variables, such as clinical trials.
The Life and Legacy of Paul Erdős
Delve into the fascinating life of Paul Erdős, a wandering mathematician whose love for numbers and collaboration reshaped the world of mathematics.
The Vulnerability of Large Language Models to the Closure of Open-Source Data Platforms
An in-depth exploration of how the closure of open-source data platforms threatens the growth of Large Language Models and the vital role humans play in this ecosystem.