Concepto de aristas múltiples empleado para esteganografía 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 users’ 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