Distributed advertisement-based BTLE presence detection reported via mqtt

Herramienta avanzada de seguimiento de IP y obtención de información de número telefónico

Component for formatting input data in realtime

The objective of this work is to detect the cell phone and/or camera used by a person in restricted areas. The paper is based on intensive image processing techniques, such as, features extraction and image classification. The dataset of images is generated with cell phone camera including positive (with cell phone) and negative (without cell phone) images. We then extract relevant features by using classical features extraction techniques including Histogram of Oriented Gradients (HOG) and Speeded up Robust Features (SURF).The extracted features are then, passed to classifier for detection. We employ Support Vector Machine (SVM), Nearest Neighbor (K-NN) and Decision tree classifier which are already trained on our dataset of training images of persons using mobile or otherwise. Finally, the detection performance in terms of error rate is compared for various combinations of feature extraction and classification techniques. Our results show that SURF with SVM classifier gives the best accuracy.

Swiss CDC Project Repository: Analyze and Predict human mobility patterns using kernel density ranking methods, and analyze the spatial-temporal trends of the activity spaces of human beings.

Cell phone holder design using OpenSCAD

Table66 @eoshackathon - Secure Data Proof with Privacy You Control

A JavaScript application that senses motion on Cell phones and Tablets that have an Accelerometer.

Track recently updated GitHub pull requests across your repositories with a lightweight macOS menubar app using SwiftUI and Tuist.