3 research outputs found
Concepto de aristas mĂșltiples empleado para esteganografĂa de imagen
Digital Steganography means hiding sensitive data inside a cover object ina way that is invisible to un-authorized persons. Many proposed steganography techniques in spatial domain may achieve high invisibility requirement but sacrifice the good robustness against attacks. In some cases, weneed to take in account not just the invisibility but also we need to thinkabout other requirement which is the robustness of recovering the embedded secrete messages. In this paper we propose a new steganoraphicscheme that aims to achieve the robustness even the stego image attackedby steganalyzers. Furthermore, we proposed a scheme which is more robust against JPEG compression attack compared with other traditionalsteganography schemes
Modified WiFi-RSS Fingerprint Technique to locate Indoors-Smartphones: FENG building at Koya University as a case study
Positioning system used for different purposes and different services, many researches are going on to find a more accurate position with low error within high performance. There are many localization solutions with different architectures, configurations, accuracies and reliabilities for both outdoors and indoors. For example, Global Navigation Satellite System (GNSS) technology has been used for outdoors. Global Positioning System (GPS) is one of the most common outdoors tracking solutions in the world, for outdoors, however, when indoors; it could not be accurately tracked users by using a GPS system. This is because, when users enters into indoors the GPS signals will no longer available due to blocked by the roof of buildings and it is no longer considered as a viable option. WiFi Positioning System (WPS) can be used as an alternative solution to define usersâ position, especially when GPS signal is not available. Further, WPS is a low cost solution, because there is no need to deploying WiFi Access Points (WAPs) in the vicinity, as they are installed to access the Internet. In this paper, specifically, WiFi-RSS Fingerprinting technique is used to locate smartphones using WAPs signals with a modified calculation. The new modified calculation is to dynamic weighting of the WAPs RSS values based on the real-live indoors structure. The achieved positioning accuracy, based on several trial experiments, is up to 6 meters via the implemented algorithm in the MALTAB
A Novel Poisoned Water Detection Method Using Smartphone Embedded Wi-Fi Technology and Machine Learning Algorithms
Water is a necessary fluid to the human body and automatic checking of its
quality and cleanness is an ongoing area of research. One such approach is to
present the liquid to various types of signals and make the amount of signal
attenuation an indication of the liquid category. In this article, we have
utilized the Wi-Fi signal to distinguish clean water from poisoned water via
training different machine learning algorithms. The Wi-Fi access points (WAPs)
signal is acquired via equivalent smartphone-embedded Wi-Fi chipsets, and then
Channel-State-Information CSI measures are extracted and converted into feature
vectors to be used as input for machine learning classification algorithms. The
measured amplitude and phase of the CSI data are selected as input features
into four classifiers k-NN, SVM, LSTM, and Ensemble. The experimental results
show that the model is adequate to differentiate poison water from clean water
with a classification accuracy of 89% when LSTM is applied, while 92%
classification accuracy is achieved when the AdaBoost-Ensemble classifier is
applied