Artificial Intelligence

Jamming devices pose a significant threat by disrupting signals from the global navigation satellite system (GNSS), compromising the robustness of accurate positioning. Detecting anomalies in frequency snapshots is crucial to counteract these interferences effectively. The ability to adapt to diverse, unseen interference characteristics is essential for ensuring the reliability of GNSS in real-world applications. We recorded a dataset with our own sensor station at a German highway with two interference classes and one non-interference class.

Osteoarthritis (OA) is a prevalent degenerative joint disease,particularly affecting the knees. Early and accurate detection of OA and its severity, often graded using the Kellgren-Lawrence (KL) scale, is crucial for timely intervention and management. This study explores the application of deep learning techniques to automatically detect OA and assign KL grades from knee X-ray images. We propose a novel deep learning architecture that effectively extracts relevant features from X-ray images and classifies them into different KL grades.

I met Yeshua Ben Joseph, Yahowah, the living Allaha in person in 2007, and this work, may it be entirely unto His grace.

The Cone of Perception 4th Edition Table of Contents

1. Introduction to the 4th Edition
2. The Meaning of Now
3. The Geometric Pattern of Perception Theorems

i. Math for Transforming a Circle into a Cone
a. Theorem 1 - Difference in the Circumferences of Two Circles

i. Lemma1 ii. Lemma 2 iii.Lemma 3 iv.Lemma 4 v. Lemma 5

b. Theorem 2 - Equilateral Triangle of Instantaneous Velocity = Average Velocity i. Lemma8

The Machine Failure Predictions Dataset (D_2) is a real-world dataset sourced from Kaggle, containing 10,000 records and 14 features pertinent to IIoT device performance and health status. The binary target feature, 'failure', indicates whether a device is functioning (0) or has failed (1). Predictor variables include telemetry readings and categorical features related to device operation and environment. Data preprocessing included aggregating features related to failure types and removing non-informative features such as Product ID.