SYSTEM FOR MONITORING DRONE POSITION INSIDE BUILDINGS
Journal Title: Scientific Bulletin, Series: Electronics and Computer Science - Year 2017, Vol 17, Issue 1
Abstract
The constant need to monitor devices has increased in recent years to the point that devices are not only tracked outside, via GPS or similar systems, but inside too, using Internal Positioning Systems. These systems are usually using Wi-Fi signal and other sensor information to determine de the smart device location. This article presents the implementation of a simple position monitor for smart devices inside buildings using Raspberry Pi as the smart device and the Wi-Fi signal strength for distance indication. Wi-Fi AP location is considered fixed for this implementation.
Authors and Affiliations
Valeriu Ionescu
Keywords
Related Articles
- EP ID EP320858
- DOI -
- Views 65
- Downloads 0
IAAS PRIVATE CLOUD COMPARISON OPENSTACK VS CLOUDSTACK
Many organizations are now investing in cloud computing because they have realized it has the ability to promote rapid growth while at the same time reducing the speed and costs of application deployment. Enterprises no...
A HIGH CAPACITIVE EMD TECHNIQUE FOR WET IMAGE
Exploiting Modification Direction (EMD) is a technique to hide secret data into digital images whereas wet paper coding is a technique for hiding data into specific pixels called dry pixels. In our scheme, we combined bo...
ALGORITHM FOR AUTOMATIC GENERATION OF INK AND CHALK ILLUSTRATIONS
This article presents an algorithm for the automatic generation of illustrations performed with chalk and ink hatching techniques. Methods for obtaining different types of hatch patterns are presented. The results obtain...
EFFECT OF ENSEMBLES ON SELECTED DATA MINING TECHNIQUES IN CLASSIFICATION OF LIVER DISEASES
The liver is a very important organ in the human body as it carries out vital functions such as clearing of toxins from the blood, metabolizing drugs, makes proteins for blood clotting amongst others. The complexity of t...
PERFORMANCE EVALUATION OF BREAST CANCER DIAGNOSIS USING RADIAL BASIS FUNCTION, C4.5 AND ADABOOST
This paper conducted a performance evaluation on the most commonly data mining algorithms: Support Vector Machines (Radial basis function), C4.5 decision tree algorithm and Adaboost, using the two previous algorithms as...