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FORECASTING OF RENEWABLE ENERGY PRODUCTION BY USING GENETIC ALGORITHM (GA) FO...u772020
My topic of thesis is about constructing prediction model of wind energy and solar energy by artificial neural network with genetic algorithm.
Because the electricity price in Nordic area is determined in advance, it is beneficial to construct prediction model for renewable energy. With the mentioned prediction model, we can construct hybrid system (wind energy and solar energy) to produce hydrogen. Then the fuel cell can produce electricity and connect to the network by using the constructed prediction model. In our prediction model, the critical parameters like 10 minutes average wind speed and deviation for wind energy could be used as historical data to train to artificial neural network (ANN). We also hope to utilize genetic algorithm (GA) to speed up the ANN training and hybrid system scale design.
Robust workload and energy management for sustainable data centersieeepondy
Robust workload and energy management for sustainable data centers
+91-9994232214,7806844441, ieeeprojectchennai@gmail.com,
www.projectsieee.com, www.ieee-projects-chennai.com
IEEE PROJECTS 2016-2017
-----------------------------------
Contact:+91-9994232214,+91-7806844441
Email: ieeeprojectchennai@gmail.com
Energy efficient cooperative transmission for simultaneous wireless informati...LogicMindtech Nologies
NS2 Projects for M. Tech, NS2 Projects in Vijayanagar, NS2 Projects in Bangalore, M. Tech Projects in Vijayanagar, M. Tech Projects in Bangalore, NS2 IEEE projects in Bangalore, IEEE 2015 NS2 Projects, WSN and MANET Projects, WSN and MANET Projects in Bangalore, WSN and MANET Projects in Vijayangar
Application of artificial neural networks for shunt active power filter controlAsoka Technologies
Artificial neural network (ANN) is becoming an attractive estimation and regression technique in many control applications due to its parallel computing nature and high learning capability. There has been a lot of effort in employing the ANN in shunt active power filter (APF) control applications. Adaptive Linear Neuron (ADALINE) and feed-forward multilayer neural network (MNN) are the most commonly used ANN techniques to extract fundamental and/or harmonic components present in the nonlinear currents. This paper aims to provide an in-depth understanding on realizing ADALINE and feed-forward MNN-based control algorithms for shunt APF. A step-by-step procedure to implement these ANN-based techniques in MATLAB/Simulink environment is provided. Furthermore, a detailed analysis on the performance, limitation, and advantages of both methods is presented in the paper. The study is supported by conducting both simulation and experimental validations.
Application of Artificial Neural Networks for Shunt APF ControlAsoka Technologies
Artificial Neural Network (ANN) is becoming an attractive estimation and regression technique in many control applications due to its parallel computing nature and high learning capability. There has been a lot of effort in employing the ANN in shunt active power filter (APF) control applications. Adaptive Linear Neuron (ADALINE) and feed-forward Multilayer Neural Network (MNN) are the most commonly used ANN techniques to extract fundamental and/or harmonic components present in the non-linear currents. This paper aims to provide an in-depth understanding on realizing ADALINE and feed-forward MNN based control algorithms for shunt APF. A step-by-step procedure to implement these ANN based techniques, in Matlab/ Simulink environment, is provided. Furthermore, a detailed analysis on the performance, limitation and advantages of both methods is presented in the paper. The study is supported by conducting both simulation and experimental validations.
Implementing Workload Postponing In Cloudsim to Maximize Renewable Energy Uti...IJERA Editor
Green datacenters has become a major research area among researchers in academy and industry. One of the
recent approaches getting higher attention is supplying datacenters with renewable sources of energy, leading to
cleaner and more sustainable datacenters. However, this path poses new challenges. The main problem with
existing renewable energy technologies is high variability, which means high fluctuation of available energy
during different time periods on a day, month or year. In our paper, we address the issue of better managing
datacenter workload in order to achieve higher utilization of available renewable energy. We implement an
algorithm in CloudSim simulator which decides to postpone or urgently run a specific job asking for datacenter
resources, based on job’s deadline and available solar energy. The aim of this algorithm is to make workload
energy consumption through 24 hours match as much as possible the solar energy availability in 24 hours. Two
typical, clear and cloudy days, are taken in consideration for simulation. The results from our experiments show
that, for the chosen workload model, jobs are better managed by postponing or urgently running them, in terms
of leveraging available solar energy. This yields up to 17% higher utilization of daily solar energy
Short Term Electrical Load Forecasting by Artificial Neural NetworkIJERA Editor
This paper presents an application of artificial neural networks for short-term times series electrical load
forecasting. An adaptive learning algorithm is derived from system stability to ensure the convergence of
training process. Historical data of hourly power load as well as hourly wind power generation are sourced from
European Open Power System Platform. The simulation demonstrates that errors steadily decrease in training
with the adaptive learning factor starting at different initial value and errors behave volatile with constant
learning factors with different values
FORECASTING OF RENEWABLE ENERGY PRODUCTION BY USING GENETIC ALGORITHM (GA) FO...u772020
My topic of thesis is about constructing prediction model of wind energy and solar energy by artificial neural network with genetic algorithm.
Because the electricity price in Nordic area is determined in advance, it is beneficial to construct prediction model for renewable energy. With the mentioned prediction model, we can construct hybrid system (wind energy and solar energy) to produce hydrogen. Then the fuel cell can produce electricity and connect to the network by using the constructed prediction model. In our prediction model, the critical parameters like 10 minutes average wind speed and deviation for wind energy could be used as historical data to train to artificial neural network (ANN). We also hope to utilize genetic algorithm (GA) to speed up the ANN training and hybrid system scale design.
Robust workload and energy management for sustainable data centersieeepondy
Robust workload and energy management for sustainable data centers
+91-9994232214,7806844441, ieeeprojectchennai@gmail.com,
www.projectsieee.com, www.ieee-projects-chennai.com
IEEE PROJECTS 2016-2017
-----------------------------------
Contact:+91-9994232214,+91-7806844441
Email: ieeeprojectchennai@gmail.com
Energy efficient cooperative transmission for simultaneous wireless informati...LogicMindtech Nologies
NS2 Projects for M. Tech, NS2 Projects in Vijayanagar, NS2 Projects in Bangalore, M. Tech Projects in Vijayanagar, M. Tech Projects in Bangalore, NS2 IEEE projects in Bangalore, IEEE 2015 NS2 Projects, WSN and MANET Projects, WSN and MANET Projects in Bangalore, WSN and MANET Projects in Vijayangar
Application of artificial neural networks for shunt active power filter controlAsoka Technologies
Artificial neural network (ANN) is becoming an attractive estimation and regression technique in many control applications due to its parallel computing nature and high learning capability. There has been a lot of effort in employing the ANN in shunt active power filter (APF) control applications. Adaptive Linear Neuron (ADALINE) and feed-forward multilayer neural network (MNN) are the most commonly used ANN techniques to extract fundamental and/or harmonic components present in the nonlinear currents. This paper aims to provide an in-depth understanding on realizing ADALINE and feed-forward MNN-based control algorithms for shunt APF. A step-by-step procedure to implement these ANN-based techniques in MATLAB/Simulink environment is provided. Furthermore, a detailed analysis on the performance, limitation, and advantages of both methods is presented in the paper. The study is supported by conducting both simulation and experimental validations.
Application of Artificial Neural Networks for Shunt APF ControlAsoka Technologies
Artificial Neural Network (ANN) is becoming an attractive estimation and regression technique in many control applications due to its parallel computing nature and high learning capability. There has been a lot of effort in employing the ANN in shunt active power filter (APF) control applications. Adaptive Linear Neuron (ADALINE) and feed-forward Multilayer Neural Network (MNN) are the most commonly used ANN techniques to extract fundamental and/or harmonic components present in the non-linear currents. This paper aims to provide an in-depth understanding on realizing ADALINE and feed-forward MNN based control algorithms for shunt APF. A step-by-step procedure to implement these ANN based techniques, in Matlab/ Simulink environment, is provided. Furthermore, a detailed analysis on the performance, limitation and advantages of both methods is presented in the paper. The study is supported by conducting both simulation and experimental validations.
Implementing Workload Postponing In Cloudsim to Maximize Renewable Energy Uti...IJERA Editor
Green datacenters has become a major research area among researchers in academy and industry. One of the
recent approaches getting higher attention is supplying datacenters with renewable sources of energy, leading to
cleaner and more sustainable datacenters. However, this path poses new challenges. The main problem with
existing renewable energy technologies is high variability, which means high fluctuation of available energy
during different time periods on a day, month or year. In our paper, we address the issue of better managing
datacenter workload in order to achieve higher utilization of available renewable energy. We implement an
algorithm in CloudSim simulator which decides to postpone or urgently run a specific job asking for datacenter
resources, based on job’s deadline and available solar energy. The aim of this algorithm is to make workload
energy consumption through 24 hours match as much as possible the solar energy availability in 24 hours. Two
typical, clear and cloudy days, are taken in consideration for simulation. The results from our experiments show
that, for the chosen workload model, jobs are better managed by postponing or urgently running them, in terms
of leveraging available solar energy. This yields up to 17% higher utilization of daily solar energy
Short Term Electrical Load Forecasting by Artificial Neural NetworkIJERA Editor
This paper presents an application of artificial neural networks for short-term times series electrical load
forecasting. An adaptive learning algorithm is derived from system stability to ensure the convergence of
training process. Historical data of hourly power load as well as hourly wind power generation are sourced from
European Open Power System Platform. The simulation demonstrates that errors steadily decrease in training
with the adaptive learning factor starting at different initial value and errors behave volatile with constant
learning factors with different values
ENERGY EFFICIENT GRID AND TREE BASED ROUTING PROTOCOLijwmn
In Wireless Sensor Network, a large number of sensor nodes are deployed and they mainly consume energy
in transmitting data over long distances. Sensor nodes are battery powered and their energy is restricted.
Since the location of the sink is remote, considerable energy would be consumed if each node directly
transmits data to the base station. Aggregating data at the intermediate nodes and transmitting using multihops
aids in reducing energy consumption to a great extent. This paper proposes a hybrid protocol
“Energy efficient Grid and Tree based routing protocol” (EGT) in which the sensing area is divided into
grids. The nodes in the grid relay data to the cell leader which aggregates the data and transmits to the
sink using the constructed hop tree. Simulation results show that EGT performs better than LEACH.
Applicability of Error Limit in Forecasting & Scheduling of Wind & Solar Powe...del2infinity Energy
A Technical paper on ‘Applicability of Error Limit in Forecasting & Scheduling of Wind & Solar Power in India’’ by Abhik Kumar Das at India SMART GRID Week 2017 organised by India Smart Grid Forum & Government of India at Manekshaw Centre, New Delhi.
A Comparative study on Different ANN Techniques in Wind Speed Forecasting for...IOSRJEEE
There are several available renewable sources of energy, among which Wind Power is the one which is most uncertain in nature. This is because wind speed changes continuously with time leading to uncertainty in availability of amount of wind power generated. Hence, a short-term forecasting of wind speed will help in prior estimation of wind power generation availability for the grid and economic load dispatch.This paper present a comparative study of a Wind speed forecasting model using Artificial Neural Networks (ANN) with three different learning algorithms. ANN is used because it is a non-linear data driven, adaptive and very powerful tool for forecasting purposes. Here an attempt is made to forecast Wind Speed using ANN with Levenberg-Marquard (LM) algorithm, Scaled Conjugate Gradient (SCG) algorithm and Bayesian Regularization (BR) algorithm and their results are compared based on their convergence speed in training period and their performance in testing period on the basis of Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Mean Square Error (MSE).A 48 hour ahead wind speed is forecasted in this work and it is compared with the measured values using all three algorithms and the best out of the three is selected based on minimum error.
DAMPAK TEKNOLOGI INFORMASI DAN KOMUNIKASI (TIK) TERHADAP PENDIDIKANMr Arieve
Saat ini di Indonesia TIK (Teknologi Informasi dan Komunikasi) sangat berkembang pesat. Dengan pesatnya perkembangan TIK tentu saja ada dampak negatif dan positif. TIK saat ini juga sangat berpengaruh terhadap pendidikan di Indonesia. salah satu butir
ENERGY EFFICIENT GRID AND TREE BASED ROUTING PROTOCOLijwmn
In Wireless Sensor Network, a large number of sensor nodes are deployed and they mainly consume energy
in transmitting data over long distances. Sensor nodes are battery powered and their energy is restricted.
Since the location of the sink is remote, considerable energy would be consumed if each node directly
transmits data to the base station. Aggregating data at the intermediate nodes and transmitting using multihops
aids in reducing energy consumption to a great extent. This paper proposes a hybrid protocol
“Energy efficient Grid and Tree based routing protocol” (EGT) in which the sensing area is divided into
grids. The nodes in the grid relay data to the cell leader which aggregates the data and transmits to the
sink using the constructed hop tree. Simulation results show that EGT performs better than LEACH.
Applicability of Error Limit in Forecasting & Scheduling of Wind & Solar Powe...del2infinity Energy
A Technical paper on ‘Applicability of Error Limit in Forecasting & Scheduling of Wind & Solar Power in India’’ by Abhik Kumar Das at India SMART GRID Week 2017 organised by India Smart Grid Forum & Government of India at Manekshaw Centre, New Delhi.
A Comparative study on Different ANN Techniques in Wind Speed Forecasting for...IOSRJEEE
There are several available renewable sources of energy, among which Wind Power is the one which is most uncertain in nature. This is because wind speed changes continuously with time leading to uncertainty in availability of amount of wind power generated. Hence, a short-term forecasting of wind speed will help in prior estimation of wind power generation availability for the grid and economic load dispatch.This paper present a comparative study of a Wind speed forecasting model using Artificial Neural Networks (ANN) with three different learning algorithms. ANN is used because it is a non-linear data driven, adaptive and very powerful tool for forecasting purposes. Here an attempt is made to forecast Wind Speed using ANN with Levenberg-Marquard (LM) algorithm, Scaled Conjugate Gradient (SCG) algorithm and Bayesian Regularization (BR) algorithm and their results are compared based on their convergence speed in training period and their performance in testing period on the basis of Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Mean Square Error (MSE).A 48 hour ahead wind speed is forecasted in this work and it is compared with the measured values using all three algorithms and the best out of the three is selected based on minimum error.
DAMPAK TEKNOLOGI INFORMASI DAN KOMUNIKASI (TIK) TERHADAP PENDIDIKANMr Arieve
Saat ini di Indonesia TIK (Teknologi Informasi dan Komunikasi) sangat berkembang pesat. Dengan pesatnya perkembangan TIK tentu saja ada dampak negatif dan positif. TIK saat ini juga sangat berpengaruh terhadap pendidikan di Indonesia. salah satu butir
Makalah Perkembangan dan Manfaat Teknologi Komputer Serta Dampak Teknologi In...Septi Windani
Makalah Perkembangan dan Manfaat Teknologi Komputer Serta Dampak Teknologi Informasi Dan Komunikasi Bagi Pelajar, kelompok 6
mata kuliah Keterampilan Komputer 1A
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
JAVA 2013 IEEE IMAGEPROCESSING PROJECT Reversible data hiding with optimal va...IEEEGLOBALSOFTTECHNOLOGIES
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
Data Hiding Using Reversibly Designed Difference-Pair MethodIJERA Editor
There is no question that data hiding has increasingly drawn extensive attention recently. This report presents a data hiding technique based on the modification of image histogram. It is fully reversible, that means, the original cover image can be recovered from the marked image, after the hidden data has been extracted. In this work, a data hiding scheme using reversibly designed difference-pair method is presented. In comparison to the previous work, since only one pixel of a pixel-pair was allowed to be modified by 1 bit in value, their embedding capacity was low. The embedding algorithm should have higher embedding capacity as this was the major drawback. Therefore it was decided to work on an algorithm which can increase embedding capacity in reversible domain. Results achieved after the execution of algorithms were compared with the existed work to draw result oriented conclusion.
In recent years the topic of steganography has become
very popular and a lot of research is being done in this field.
Reversible data hiding is a method in which the image in which
the encrypted data is hidden is losslessly recovered. In this paper
we have proposed such a method that the previous work done
limitations can be overcome. In this paper, we embed the
encrypted data in the image by using LSB matching technique for
reserving room, so that fast, optimal and lossless steganography
is achieved. The proposed method provides total reversibility,
that is, data extraction and image recovery.
Reversible Data Hiding in Encrypted color images by Reserving Room before Enc...ijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
A comparative case study on the various techniques of reversible data hidingIJLT EMAS
Secured transmission of data is an important concern
now a days. A number of techniques are available for hiding and
transmitting the data through the images. It may be a simple or a
complex technique. In the receiver part if the host image is
recovered without any loss it is said to be reversible data hiding.
In this paper it is proposed to compare the reversible and lossless
data hiding techniques where both the host image and the hidden
data can be recovered without any loss. There are many
techniques that is presented as block based, pixel based and
interpolation based techniques. In the past data was embedded in
encrypted images with irreversible data hiding and now it is
reversible .The PSNR and the data hiding capacity que for these
methods are compared in this paper.
A Review on Reversible Data Hiding Scheme by Image Contrast EnhancementIJERA Editor
In present world demand of high quality images, security of the information on internet is one of the most important issues of research. Data hiding is a method of hiding a useful information by embedding it on another image (cover image) to provide security and only the authorize person is able to extract the original information from the embedding data. This paper is a review which describes several different algorithms for Reversible Data Hiding (RDH). Previous literature has shown that histogram modification, histogram equalization (HE) and interpolation are the most common methods for data hiding. To improve security these methods are used in encrypted images. This paper is a comprehensive study of all the major reversible data hiding approaches implemented as found in the literature.
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
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2nd Floor,
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Chennai-26.
Efficient Reversible Data Hiding Algorithms Based on Dual Predictionsipij
In this paper, a new reversible data hiding (RDH) algorithm that is based on the concept of shifting of
prediction error histograms is proposed. The algorithm extends the efficient modification of prediction
errors (MPE) algorithm by incorporating two predictors and using one prediction error value for data
embedding. The motivation behind using two predictors is driven by the fact that predictors have different
prediction accuracy which is directly related to the embedding capacity and quality of the stego image. The
key feature of the proposed algorithm lies in using two predictors without the need to communicate
additional overhead with the stego image. Basically, the identification of the predictor that is used during
embedding is done through a set of rules. The proposed algorithm is further extended to use two and three
bins in the prediction errors histogram in order to increase the embedding capacity. Performance
evaluation of the proposed algorithm and its extensions showed the advantage of using two predictors in
boosting the embedding capacity while providing competitive quality for the stego image.
Automatic DNA Sequence Generation for Secured Effective Multi -Cloud StorageIOSR Journals
Abstract: The main target of this paper is to propose an algorithm to implement data hiding in DNA sequences
to increase the confidentiality and complexity by using software point of view in cloud computing environments.
By utilizing some interesting features of DNA sequences, the implementation of a data hiding is applied in
cloud. The algorithm which has been proposed here is based on binary coding and complementary pair rules.
Therefore, DNA reference sequence is chosen and a secret data M is hidden into it as well. As result of applying
some steps, M´´´ is come out to upload to cloud environments. The process of identifying and extracting the
original data M, hidden in DNA reference sequence begins once clients decide to use data. Furthermore,
security issues are demonstrated to inspect the complexity of the algorithm. In addition, providing better privacy
as well as ensure data availability, can be achieved by dividing the user’s data block into data pieces and
distributing them among the available SPs in such a way that no less than a threshold number of SPs can take
part in successful retrieval of the whole data block. In this paper, we propose a secured cost-effective multicloud
storage (SCMCS) model in cloud computing which holds an economical distribution of data among the
available SPs in the market, to provide customers with data availability as well as secure storage.
Keywords: DNA sequence; DNA base pairing rules; complementary rules; DNA binary coding; cloud service
provider.
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
How to Build a Module in Odoo 17 Using the Scaffold MethodCeline George
Odoo provides an option for creating a module by using a single line command. By using this command the user can make a whole structure of a module. It is very easy for a beginner to make a module. There is no need to make each file manually. This slide will show how to create a module using the scaffold method.
Safalta Digital marketing institute in Noida, provide complete applications that encompass a huge range of virtual advertising and marketing additives, which includes search engine optimization, virtual communication advertising, pay-per-click on marketing, content material advertising, internet analytics, and greater. These university courses are designed for students who possess a comprehensive understanding of virtual marketing strategies and attributes.Safalta Digital Marketing Institute in Noida is a first choice for young individuals or students who are looking to start their careers in the field of digital advertising. The institute gives specialized courses designed and certification.
for beginners, providing thorough training in areas such as SEO, digital communication marketing, and PPC training in Noida. After finishing the program, students receive the certifications recognised by top different universitie, setting a strong foundation for a successful career in digital marketing.
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
How to Add Chatter in the odoo 17 ERP ModuleCeline George
In Odoo, the chatter is like a chat tool that helps you work together on records. You can leave notes and track things, making it easier to talk with your team and partners. Inside chatter, all communication history, activity, and changes will be displayed.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Normal Labour/ Stages of Labour/ Mechanism of LabourWasim Ak
Normal labor is also termed spontaneous labor, defined as the natural physiological process through which the fetus, placenta, and membranes are expelled from the uterus through the birth canal at term (37 to 42 weeks
The simplified electron and muon model, Oscillating Spacetime: The Foundation...RitikBhardwaj56
Discover the Simplified Electron and Muon Model: A New Wave-Based Approach to Understanding Particles delves into a groundbreaking theory that presents electrons and muons as rotating soliton waves within oscillating spacetime. Geared towards students, researchers, and science buffs, this book breaks down complex ideas into simple explanations. It covers topics such as electron waves, temporal dynamics, and the implications of this model on particle physics. With clear illustrations and easy-to-follow explanations, readers will gain a new outlook on the universe's fundamental nature.
Group Presentation 2 Economics.Ariana Buscigliopptx
Matlab reversible data hiding with optimal value transfer
1. REVERSIBLE DATA HIDING WITH OPTIMAL VALUE TRANSFER
ABSTRACT:
In reversible data hiding techniques, the values of host data are modified according to some
particular rules and the original host content can be perfectly restored after extraction of the
hidden data on receiver side. In this paper, the optimal rule of value modification under a
payload-distortion criterion is found by using an iterative procedure, and a practical reversible
data hiding scheme is proposed. The secret data, as well as the auxiliary information used for
content recovery, are carried by the differences between the original pixel-values and the
corresponding values estimated from the neighbors.
The estimation errors are modified according to the optimal value transfer rule. Also, the host
image is divided into a number of pixel subsets and the auxiliary information of a subset is
always embedded into the estimation errors in the next subset. A receiver can successfully
extract the embedded secret data and recover the original content in the subsets with an inverse
order. This way, a good reversible data hiding performance is achieved.
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