Artificial Intelligence
This dataset enables advanced Wi-Fi sensing applications, including multi-subject monitoring for home surveillance, remote healthcare, and entertainment. It focuses on Beamforming Feedback Information (BFI) as a proxy for Channel State Information (CSI), eliminating the need for firmware modifications and enabling single-capture data collection across multiple channels between an access point (AP) and stations (STAs).
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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.
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This dataset contains original and augmented versions of the Korean Call Content Vishing (KorCCVi v2) dataset used in the study titled, "Enhancing Voice Phishing Detection Using Multilingual Back-Translation and SMOTE: An Empirical Study." The dataset addresses challenges of data imbalance and asymmetry in Korean voice phishing detection, leveraging data augmentation techniques such as multilingual back-translation (BT) with English, Chinese, and Japanese as intermediate languages, and Synthetic Minority Oversampling Technique (SMOTE).
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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.
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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
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Thanks be to Yeshua ben Joseph, Yahowah, the living One Allaha.
This work is a attempt to describe various braches of mathematics and the analogies betwee them. Namely:
1) Symbolic Analogic 2) Lateral Algebraic Expressions 3) Calculus of Infin- ity Tensors Energy Number Synthesis 4) Perturbations in Waves of Calculus Structures (Group Theory of Calculus) 5) Algorithmic Formation of Symbols (Encoding Algorithms)
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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.
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Este proyecto se desarrolló para optimizar el análisis de suelos agrícolas en cultivos de papa, con un enfoque en mejorar la precisión y accesibilidad de los diagnósticos de nutrientes esenciales (NPK) a través de tecnología de sensores. En primer lugar, se realizó la calibración de sensores industriales multiparámetro (CWT para NPK y CWT-multiparámetro), basándose en valores de referencia de laboratorios convencionales, lo que permitió configurar un marco de medición confiable.
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The SINEW 15 Biomarker dataset was extracted from the sensor data collected by a longitudinal study called Sensors IN-home for Elder Wellbeing (SINEW).
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