Optimality in Distortion Control in Reversible Watermarking Using Genetic Algorithms

2017 ◽  
Vol 17 (03) ◽  
pp. 1750013 ◽  
Author(s):  
Santi P. Maity ◽  
Hirak Kumar Maity
Keyword(s):  
Genetic Algorithms ◽  
Reversible Watermarking ◽  
Operating Point ◽  
Closed Form Expression ◽  
Optimal Basis ◽  
Hiding Capacity ◽  
Distortion Control ◽  
Simulation Results ◽  
High Embedding Capacity ◽  
Hidden Data

This paper proposes a reversible contrast mapping (RCM)-based reversible watermarking (RW) algorithm (RCM-RW) where optimal distortion thresholds adaptive to the image characteristics are used. A generalized form of RCM is developed using a set of transformation functions; each one may be considered as a point on a straight line, called here as operating point. Each operating point offers trade-off benefits on embedding distortion, data hiding capacity and security for the hidden data. The choice of an operating point is governed by adaptive distortion control thresholds, the values depend on the partitioning of the images on smooth, texture and edge regions. However, region-specific optimal distortion threshold is difficult to represent in closed-form expression. Genetic algorithms (GAs), due to their complex searching nature, are used here for calculating the set of distortion thresholds. Simulation results show that high embedding capacity, improved security and imperceptibility of the hidden information can be met simultaneously using a particular combination of such operating points on optimal basis. Extensive simulation results show that the proposed method outperforms the existing RW techniques.

scholarly journals Reversible Data Hiding for DICOM Image Using Lifting and Companding

Cryptography ◽  
2019 ◽  
Vol 3 (3) ◽  
pp. 21 ◽  
Author(s):  
Amit Phadikar ◽  
Poulami Jana ◽  
Himadri Mandal
Keyword(s):  
Data Hiding ◽  
Reversible Watermarking ◽  
Discrete Wavelet ◽  
High Fidelity ◽  
Embedding Capacity ◽  
Dicom Image ◽  
Hiding Capacity ◽  
Novel Approach ◽  
High Embedding Capacity ◽  
Location Map

In this work, a reversible watermarking technique is proposed for DICOM (Digital Imaging and Communications in Medicine) image that offers high embedding capacity (payload), security and fidelity of the watermarked image. The goal is achieved by embedding watermark based on companding in lifting based discrete wavelet transform (DWT) domain. In the embedding process, the companding technique is used to increase the data hiding capacity. On the other hand, a simple linear function is used in companding to make the scheme easy to implement, and content dependant watermark is used to make the scheme robust to collusion operation. Moreover, unlike previously proposed reversible watermarking techniques, this novel approach does not embed the location map in the host image that ultimately helps to achieve high fidelity of the watermarked image. The advantage of the proposed scheme is demonstrated by simulation results and also compared with selected other related schemes.

scholarly journals Detection of Attacks on Wireless Sensor Network Using Genetic Algorithms Based on Fuzzy

2019 ◽  
Vol 8 (1) ◽  
pp. 57 ◽  
Author(s):  
Shaymaa Al Hayali ◽  
Osman Ucan ◽  
Javad Rahebi ◽  
Oguz Bayat
Keyword(s):  
Genetic Algorithms ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Energy Development ◽  
Wireless Sensor ◽  
Optimal Routing ◽  
Fuzzy Function ◽  
Renewable Energy Development ◽  
Multi Agent ◽  
Simulation Results

In this paper an individual - suitable function calculating design for WSNs is conferred. A multi-agent- located construction for WSNs is planned and an analytical type of the active combination is built for the function appropriation difficulty. The purpose of this study is to identify the threats identified by clustering genetic algorithms in clustering networks, which will prolong network lifetime. In addition, optimal routing is done using the fuzzy function. Simulation results show that the simulated genetic algorithm improves diagnostic speed and improves energy consumption.©2019. CBIORE-IJRED. All rights reservedArticle History: Received May 16th 2018; Received in revised form Octiber 6th 2018; Accepted Jnauary 6th 2019; Available onlineHow to Cite This Article: Al-Hayali, S., Ucan, O., Rahebi, J. and Bayat, O. (2019) Detection of Attacks on Wireless Sensor Network Using Genetic Algorithms Based on Fuzzy. International Journal of Renewable Energy Development, 8(1), 57-64.https://doi.org/10.14710/ijred.8.1.57-64

scholarly journals Performance Analysis of a Polling-Based Access Control Combining with the Sleeping Schema in V2I VANETs for Smart Cities

2019 ◽  
Vol 11 (2) ◽  
pp. 503 ◽  
Author(s):  
Min He ◽  
Zheng Guan ◽  
Liyong Bao ◽  
Zhaoxu Zhou ◽  
Marco Anisetti ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Access Control ◽  
Smart Cities ◽  
Theoretical Calculations ◽  
Performance Characteristics ◽  
Sensor Nodes ◽  
Traffic Load ◽  
Ieee 802.11P ◽  
Closed Form Expression ◽  
Simulation Results

In vehicular ad hoc networks (VANETs), one of the important challenges is the lack of precise mathematical modeling taking into account the passive vacation triggered by the zero-arrival state of nodes. Therefore, a polling-based access control is proposed in this paper using a sleeping schema to meet the challenge of quality of service (QoS) and energy-efficient transport in VANET environments for smart cities. Based on IEEE 802.11p, it was developed in an attempt to improve the energy efficiency of the hybrid coordination function of controlled channel access (HCCA) through a self-managing sleeping mechanism for both the roadside unit (RSU) and on-board units (OBUs) or sensor nodes according to the traffic load in vehicle -to-infrastructure (V2I) scenarios. Additionally, a Markov chain was developed for analyzing the proposed mechanism, and the exact mathematical model is provided with regard to the passive vacation. Then, the performance characteristics—including the mean cyclic period, delay, and queue length—were accurately obtained. In addition, the closed-form expression of the quantitative relationship among sleeping time, performance characteristics, and service parameters was obtained, which can easily evaluate the energy efficiency. It was proven that theoretical calculations were completely consistent with simulation results. The simulation results demonstrate that the suggested method had much lower energy consumption than the standard strategy at the expense of rarely access delay.

scholarly journals An Optimized Steganography Hiding Capacity and Imperceptibly Using Genetic Algorithms

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 133496-133508
Author(s):  
Ranyiah Wazirali ◽  
Waleed Alasmary ◽  
Mohamed M. E. A. Mahmoud ◽  
Ahmad Alhindi
Keyword(s):  
Genetic Algorithms ◽  
Hiding Capacity

scholarly journals Design Coverage Optimization Based on Position of Constellations and Cost of the Launch Vehicle

2021 ◽  
Vol 17 ◽  
pp. 1160-1190
Author(s):  
Saeid Kohani ◽  
Peng Zong ◽  
Fengfan Yang
Keyword(s):  
Genetic Algorithms ◽  
Optimal Design ◽  
Launch Vehicle ◽  
Multiple Objective ◽  
Satellite Constellations ◽  
Coverage Optimization ◽  
Satellite Constellation ◽  
Constellation Design ◽  
Global Coverage ◽  
Simulation Results

This research will analyze the tradeoffs between coverage optimization based on Position dilution of precision (PDOP) and cost of the launch vehicle. It adopts MATLAB and STK tools along with multiple objective genetic algorithms (MOGA) to explore the trade space for the constellation designs at different orbital altitudes. The objective of optimal design solutions is inferred to determine the economic and efficient LEO, MEO, HEO or hybrid constellations and simulation results are presented to optimize the design of satellite constellations. The benefits of this research are the optimization of satellite constellation design, which reduces costs and increases regional and global coverage with the least number of satellites. The result of this project is the optimization of the number of constellation satellites in several orbital planes in LEO orbit. Validations are based on reviewing the results of several simulations. The results of graphs and tables are presented in the last two sections and are taken from the results of several simulations.

scholarly journals Two-Bit Embedding Histogram-Prediction-Error Based Reversible Data Hiding for Medical Images with Smooth Area

Computers ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 152
Author(s):  
Ching-Yu Yang ◽  
Ja-Ling Wu
Keyword(s):  
Data Hiding ◽  
Prediction Error ◽  
Reversible Data Hiding ◽  
Medical Images ◽  
High Capacity ◽  
Personal Privacy ◽  
Hiding Capacity ◽  
X Ray ◽  
Magnetic Resonance Imaging Mri ◽  
Hidden Data

During medical treatment, personal privacy is involved and must be protected. Healthcare institutions have to keep medical images or health information secret unless they have permission from the data owner to disclose them. Reversible data hiding (RDH) is a technique that embeds metadata into an image and can be recovered without any distortion after the hidden data have been extracted. This work aims to develop a fully reversible two-bit embedding RDH algorithm with a large hiding capacity for medical images. Medical images can be partitioned into regions of interest (ROI) and regions of noninterest (RONI). ROI is informative with semantic meanings essential for clinical applications and diagnosis and cannot tolerate subtle changes. Therefore, we utilize histogram shifting and prediction error to embed metadata into RONI. In addition, our embedding algorithm minimizes the side effect to ROI as much as possible. To verify the effectiveness of the proposed approach, we benchmarked three types of medical images in DICOM format, namely X-ray photography (X-ray), computer tomography (CT), and magnetic resonance imaging (MRI). Experimental results show that most of the hidden data have been embedded in RONI, and the performance achieves high capacity and leaves less visible distortion to ROIs.

scholarly journals Injection-lock and reconfigurable charge-domain sampling mixers/filters for data communications over wireless channels.

2021 ◽  
Author(s):  
Yushi Zhou
Keyword(s):  
Impulse Response ◽  
Software Defined Radio ◽  
Wireless Channels ◽  
Injection Locking ◽  
Harmonic Oscillators ◽  
Closed Form Expression ◽  
Infinite Impulse Response ◽  
Mathematical Treatment ◽  
Data Communications ◽  
Simulation Results

This thesis provides a theoretical and experimental study of injection locking and reconfigurable charge-domain sampling mixers and filters for data communications over wireless channels. On injection-locking, the intrinsic relation between the characteristics of injection signals such as sinusoidal or square, single-tone or multi-tone, the type of oscillators under injection such as harmonic oscillators (passive or active LC oscillators) or non-harmonic oscillators (ring or relaxation oscillators), and the lock range of the oscillators under injection was investigated. For the very first time, we discovered the intrinsic relation between the lock range and the phase of multiple injections of harmonic oscillators. In addition, we obtained the closed-form expression of the lock range of harmonic oscillators with square-wave injections. Moreover, we obtained the distinct characteristics of the lock range of harmonic and non-harmonic oscillators and that of different types of non-harmonic oscillators. These theoretical findings were not known before and were validated using simulation results. On reconfigurable charge-domain sampling mixers and filters for software-defined radio, a novel quadrature charge-domain down-conversion sampling mixer with embedded finite-impulse-response (FIR), infinite-impulse-response (IIR), and 4-path bandpass filters was developed. An in-depth investigation of the principles of periodic impulse sampling, periodic windowed sampling, and periodic N-path windowed sampling was presented and a detailed mathematical treatment of charge-domain windowed samplers with built-in sinc, FIR and IIR filters was provided. The proposed quadrature charge-domain sampler with embedded FIR, IIR, and 4-path band-pass filters was implemented in IBM 130 nm 1.2V CMOS technology and its performance was validated both using simulation results and on-wafer measurement.

scholarly journals A Comparative and Experimental Study on Gradient and Genetic Optimization Algorithms for Parameter Identification of Linear MIMO Models of a Drilling Vessel

2015 ◽  
Vol 25 (4) ◽  
pp. 877-893 ◽  
Author(s):  
Stanisław Bańka ◽  
Michał Brasel ◽  
Paweł Dworak ◽  
Krzysztof Jaroszewski
Keyword(s):  
Experimental Study ◽  
Genetic Algorithms ◽  
Parameter Identification ◽  
Nonlinear Model ◽  
Operating Point ◽  
Model Parameters ◽  
Genetic Optimization ◽  
Advantages And Disadvantages ◽  
Current Operating ◽  
Current Operating Point

Abstract The paper presents algorithms for parameter identification of linear vessel models being in force for the current operating point of a ship. Advantages and disadvantages of gradient and genetic algorithms in identifying the model parameters are discussed. The study is supported by presentation of identification results for a nonlinear model of a drilling vessel.

scholarly journals Statistical modeling and optimal energy distribution of cogeneration units by genetic algorithms

2022 ◽  
Vol 12 (2) ◽  
pp. 1131
Author(s):  
Othmane Maakoul ◽  
Hamid El Omari ◽  
Aziza Abid
Keyword(s):  
Artificial Intelligence ◽  
Genetic Algorithms ◽  
Energy Management ◽  
Cost Minimization ◽  
Operating Cost ◽  
Optimization Criterion ◽  
Efficiency Improvement ◽  
Fuel Cost ◽  
Gas Emissions ◽  
Simulation Results

Our main objective is to evaluate the performance of a new method to optimize the energy management of a production system composed of six cogeneration units using artificial intelligence. The optimization criterion is economic and environmental in order to minimize the total fuel cost, as well as the reduction of polluting gas emissions such as COx, NOx and SOx. First, a statistical model has been developed to determine the power that the cogeneration units can provide. Then, an economic model of operation was developed: fuel consumption and pollutant gas emissions as a function of the power produced. Finally, we studied the energy optimization of the system using genetic algorithms (GA), and contribute to the research on improving the efficiency of the studied power system. The GA has a better optimization performance, it can easily choose satisfactory solutions according to the optimization objectives, and compensate for these defects using its own characteristics. These characteristics make GA have outstanding advantages in iterative optimization. The robustness of the proposed algorithm is validated by testing six cogeneration units, and the obtained simulation results of the proposed system prove the value and effectiveness of GA for efficiency improvement as well as operating cost minimization.

Application of Genetic Algorithms for Optimization of Anycast Routing in Delay and Disruption Tolerant Networks

2012 ◽  
pp. 205-222
Author(s):  
Éderson R. Silva ◽  
Paulo R. Guardieiro
Keyword(s):  
Genetic Algorithms ◽  
Rural Areas ◽  
Routing Algorithm ◽  
Data Communication ◽  
Disruption Tolerant Networks ◽  
Long Duration ◽  
Anycast Routing ◽  
Minimum Delay ◽  
Simulation Results ◽  
Networking Technology

Delay and disruption tolerant networks (DTNs) have the capacity of providing data communication to remote and rural areas where current networking technology does not work well. In such challenging areas characterized by long duration partition, routing is a common problem. Anycast routing can be used for many applications in DTNs, and it is useful when nodes wish to send messages to at least one, and preferably only one, of the members in an anycast destination group. In this chapter, an anycast routing algorithm for DTNs based on genetic algorithms (GAs) is presented and analyzed. The GA is applied to find the appropriate combination of each path to comply with the delivery needs of the group of anycast sessions simultaneously. The routing algorithm based on GAs under consideration uses the concept of subpopulation to produce the next generation of the population, a limited number of solutions to be evaluated, and yields minimum delay in achieving a specified rate of delivery. Simulation results show that the studied GA-based anycast routing algorithm can produce good results.

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