El uso de bloques de imagen en el dominio espacial como una vía robusta de estenografía

Steganography is a way to convey secret communication, with rapid electronic communication and high demand of using the internet, steganography has become a wide field of research and discussion. In this paper a new approach for hiding information in cover image proposed in spatial domain, the proposed approach divides the host image into blocks of size (8x8) pixels and message bits are embeds into the pixels of a cover image. The 64-pixel values of each block converted to be represented in binary system and compared with corresponding secret data bits for finding the matching and hold 6-pixels. The search process performed by comparing each secret data bit (8-bits) with created binary plane at the cover image, if matching is found the last row of the created binary plane which is (LSB) is modified to indicate the location of the matched bits sequence “which is the secret data” and number of the row, if matching is not found in all 7th rows the secret sequence is copied in to the corresponding 8th row location.The payload of this technique is 6 pixels’ message (48-bits) in each block. In the experiments secret messages are randomly embedded into different images. The quality of the stego-image from which the original text message is extracted is not affected at all. For validation of the presented mechanism, the capacity, the circuit complexity, and the measurement of distortion against steganalysis is evaluated using the peak-signal-to-noise ratio (PSNR) are analyzed

Human gait identification using Kinect sensor

This study investigates a novel three-dimension gait recognition approach based on skeleton representation of motion by the cheap consumer level camera Kinect sensor. In this work, a new exemplification of human gait signature is proposed using the spatio-temporal variations in relative angles among various skeletal joints and changing of measured distance between limbs and land. These measurements are computed during one gait cycle. Further, we have created our own dataset based on Kinect sensor and extract two sets of dynamic features. Nearest Neighbors and Linear Discriminant Classifier (LDC) are used for classification. The results of the experiments show the proposed approach as an effective and human gait recognizer in comparison with current Kinect-based gait recognition methods

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

Seamless Outdoors-Indoors Localization Solutions on Smartphones: Implementation and Challenges

© ACM, 2016. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in http://doi.org/10.1145/2871166[EN] The demand for more sophisticated Location-Based Services (LBS) in terms of applications variety and accuracy is tripling every year since the emergence of the smartphone a few years ago. Equally, smartphone manufacturers are mounting several wireless communication and localization technologies, inertial sensors as well as powerful processing capability, to cater to such LBS applications. A hybrid of wireless technologies is needed to provide seamless localization solutions and to improve accuracy, to reduce time to fix, and to reduce power consumption. The review of localization techniques/technologies of this emerging field is therefore important. This article reviews the recent research-oriented and commercial localization solutions on smartphones. The focus of this article is on the implementation challenges associated with utilizing these positioning solutions on Android-based smartphones. Furthermore, the taxonomy of smartphone-location techniques is highlighted with a special focus on the detail of each technique and its hybridization. The article compares the indoor localization techniques based on accuracy, utilized wireless technology, overhead, and localization technique used. The pursuit of achieving ubiquitous localization outdoors and indoors for critical LBS applications such as security and safety shall dominate future research efforts.This research was sponsored by Koya University, Kurdistan Region-Iraq. The authors also would like to thank Dr. Ali Al-Sherbaz (from the University of Northampton-UK) and Dr. Naseer Al-Jawad (from the University of Buckingham-UK) for providing and improving the quality of this article in terms of academic and technical writing.Maghdid, HS.; Lami, IA.; Ghafoor, KZ.; Lloret, J. (2016). 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Enabling accurate indoor localization for different platforms for smart cities using a transfer learning algorithm

This is an accepted manuscript of an article published by Wiley in Internet Technology Letters on 17/09/2020, available online: https://doi.org/10.1002/itl2.200 The accepted version of the publication may differ from the final published version.Indoor localization algorithms in smart cities often use Wi‐Fi fingerprints as a database of Received Signal Strength (RSS) and its corresponding position coordinate for position estimation. However, the issue of fingerprinting is the use of different platform‐devices. To this end, we propose a Long Short‐Term Memory (LSTM)‐based novel indoor positioning mechanism in smart city environment. We used LSTM, a type of recurrent neural network to process sequential data of users’ trajectory in indoor buildings. The proposed approach first utilizes a database of normalizing fingerprint landmarks to calculateWiFi Access Points (WAPs) RSS values to mitigate the fluctuation issue and then apply the normalization parameters on the RSS values during the online phase. Afterwards, we constructed a transfer model to adapt the RSS values during the offline phase and then applying it on the RSS values from the different smartphones during the online phase. Thorough simulation results confirm that the proposed approach can obtain 1.5 to 2 meters positioning accuracy for indoor environments, which is 60 % higher than traditional approaches

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

Analysis of Encryption Algorithms Proposed for Data Security in 4G and 5G Generations

This paper intended to investigate and analyze encryption algorithms that can be proposed to perform data security in 4G and 5G networks. The research explores different standards, services, and features presented via 4G and 5G networks. Also, the basic components of encryption algorithms (e.g., ZUC, SNOW 3G, and AES) are investigated. For instance, initialization keys have been identified and analyzed due to their vital roles in determining the security of the encryption algorithms. Moreover, the researchers analyzed the effective elements of these algorithms (i.e., LFSR registers, substitution boxes, NLF functions like finite state machines, Math transformations, secret encryption keys, and non-secret IV keys). Cryptanalysis methods play important roles in determining the security of these algorithms. Thus, some cryptanalysis methods have been explored and investigated. Various weak points have been identified in initialization process of these algorithms. Therefore, different recommendations are presented that enhance the security of these ciphers, and can be reflected in data security in 4G and 5G networks

Novel Integration of Wi-Fi Signal and Magnetometer Sensor Measurements in Fingerprinting Technique for Indoors Smartphone positioning

Smartphones are becoming more widespread, and location-based services (LBS) have become one of the most important uses in people’s daily lives. While outdoor location is reasonably simple thanks to GNSS signals, however, indoor location is more problematic due to the lack of GNSS signals. As a result of the widespread deployment of alternative technologies such as wireless and sensors technologies, various studies on wireless-based indoor positioning have been conducted. However, each technology has its own limitations including multipath fading of wireless signals causes time-varying received signal strength as well as the accumulated error of the onboard sensors (i.e. sensor drift) resulting in poor localization accuracy. Motivated by these restrictions, this work integrates the applicability of two technologies for indoor positioning that are already available in smartphones by avoiding their limitation. The integration is based on fingerprinting-positioning technique by including magnetometer sensor measurements and WiFi signal strength. Android-based smartphones with low-cost sensors in real indoor scenarios are utilized to create a dataset and collect independent track tests to confirm results. The performance of different scenarios, such as Wi-Fi alone, magnetometer alone, and magnetometer-aided Wi-Fi, is compared. The experimental results show that the combination of magnetometer sensor and WiFi signal strength provides significant results in which leads to reducing the location error to 0.7224 meters