Showing papers in "International Journal of Electrical and Computer Engineering in 2019"

Word2Vec model for sentiment analysis of product reviews in Indonesian language

Abstract: Online product reviews have become a source of greatly valuable information for consumers in making purchase decisions and producers to improve their product and marketing strategies However, it becomes more and more difficult for people to understand and evaluate what the general opinion about a particular product in manual way since the number of reviews available increases Hence, the automatic way is preferred One of the most popular techniques is using machine learning approach such as Support Vector Machine (SVM) In this study, we explore the use of Word2Vec model as features in the SVM based sentiment analysis of product reviews in Indonesian language The experiment result show that SVM can performs well on the sentiment classification task using any model used However, the Word2vec model has the lowest accuracy (only 070), compared to other baseline method including Bag of Words model using Binary TF, Raw TF, and TFIDF This is because only small dataset used to train the Word2Vec model Word2Vec need large examples to learn the word representation and place similar words into closer position

IoT based car accident detection and notification algorithm for general road accidents

Abstract: With an increase in population, there is an increase in the number of accidents that happen every minute. These road accidents are unpredictable. There are situations where most of the accidents could not be reported properly to nearby ambulances on time. In most of the cases, there is the unavailability of emergency services which lack in providing the first aid and timely service which can lead to loss of life by some minutes. Hence, there is a need to develop a system that caters to all these problems and can effectively function to overcome the delay time caused by the medical vehicles. The purpose of this paper is to introduce a framework using IoT, which helps in detecting car accidents and notifying them immediately. This can be achieved by integrating smart sensors with a microcontroller within the car that can trigger at the time of an accident. The other modules like GPS and GSM are integrated with the system to obtain the location coordinates of the accidents and sending it to registered numbers and nearby ambulance to notify them about the accident to obtain immediate help at the location.

Direct and indirect vector control of a doubly fed induction generator based in a wind energy conversion system

Abstract: In the recent years, the development and the exploitation of renewable energy knew a great evolution. Among these energy resources, the wind power represents an important potential for that the wind system has been the subject of several researches. The purpose of this study is to improve the power extracted from wind energy, taking into consideration the variation of wind speed which causes a problem in energy production. For this purpose, we have controlled the powers whether it is active or reactive delivered by the generator. This paper, presents essentially the modeling and control of doubly- fed induction generator (DFIG), which is connected to a variable speed wind turbine. Firstly, the model of the wind power system with the maximum power point tracking (MPPT) strategy is shown. Then, the modeling of doubly- fed induction generator (DFIG) and its power control is presented. Finnaly, to ensure the attitude of these controls the simulations is presented in the Matlab/Simulink environment.

A review of remotely sensed satellite image classification

Abstract: Satellite image classification has a vital role for the extraction and analysis of the useful satellite image information. This paper comprises the study of the satellite images classification and Remote Sensing along with a brief overview of the previous studies that are proposed in this field. In this paper, the existing work has been explained utilizing the classification techniques on satellite images of Alwar region in India that covers decent land cover features like Vegetation, Water, Urban, Barren, and Rocky regions. The post- implementation of the classification algorithms, the classified image is obtained displaying different classes that are represented by different colours. Each feature is represented by a different colour and can be easily perceived from the image obtained after classification. The focus of this study is on enhancing the classification accuracy by using proper classifiers along with the novel feature extraction techniques and pre-processing steps. Work of different authors is being discussed in a tabular form defining the methods and outcomes of the respective studies.

Earthquake trend prediction using long short-term memory RNN

Abstract: The prediction of a natural calamity such as earthquakes has been an area of interest for a long time but accurate results in earthquake forecasting have evaded scientists, even leading some to deem it intrinsically impossible to forecast them accurately. In this paper an attempt to forecast earthquakes and trends using a data of a series of past earthquakes. A type of recurrent neural network called Long Short-Term Memory (LSTM) is used to model the sequence of earthquakes. The trained model is then used to predict the future trend of earthquakes. An ordinary Feed Forward Neural Network (FFNN) solution for the same problem was done for comparison. The LSTM neural network was found to outperform the FFNN. The R^2 score of the LSTM is better than the FFNN’s by 59%.

Computer Vision Based 3D Reconstruction : A Review

Abstract: 3D reconstruction are used in many fields starts from the object reconstruction such as site, and cultural artifacts in both ground and under the sea levels. The scientist are beneficial for these task in order to learn and keep the environment into 3D data due to the extinction. In this paper explained vision setup that is commonly used such as single camera, stereo camera, Kinect / Structured Light/ Time of Flight camera and fusion approach. The prior works also explained how the 3D reconstruction perform in many fields and using various algorithms.

Opinion mining on newspaper headlines using SVM and NLP

Abstract: Opinion Mining also known as Sentiment Analysis, is a technique or procedure which uses Natural Language processing (NLP) to classify the outcome from text. There are various NLP tools available which are used for processing text data. Multiple research have been done in opinion mining for online blogs, Twitter, Facebook etc. This paper proposes a new opinion mining technique using Support Vector Machine (SVM) and NLP tools on newspaper headlines. Relative words are generated using Stanford CoreNLP, which is passed to SVM using count vectorizer. On comparing three models using confusion matrix, results indicate that Tf-idf and Linear SVM provides better accuracy for smaller dataset. While for larger dataset, SGD and linear SVM model outperform other models.

Comparative Review of the Features of Automated Software Testing Tools

Abstract: Software testing is considered to be one of the most important processes in software development for it verifies if the system meets the user requirements and specification. Manual testing and automated testing are two ways of conducting software testing. Automated testing gives software testers the ease to automate the process of software testing thus considered more effective when time, cost and usability are concerned. There are a wide variety of automated testing tools available, either open source or commercial. This paper provides a comparative review of features of open source and commercial testing tools that may help users to select the appropriate software testing tool based on their requirements.

Recognition of emotional states using EEG signals based on time-frequency analysis and SVM classifier

Abstract: The recognition of emotions is a vast significance and a high developing field of research in the recent years. The applications of emotion recognition have left an exceptional mark in various fields including education and research. Traditional approaches used facial expressions or voice intonation to detect emotions, however, facial gestures and spoken language can lead to biased and ambiguous results. This is why, researchers have started to use electroencephalogram (EEG) technique which is well defined method for emotion recognition. Some approaches used standard and pre-defined methods of the signal processing area and some worked with either fewer channels or fewer subjects to record EEG signals for their research. This paper proposed an emotion detection method based on time-frequency domain statistical features. Box-and-whisker plot is used to select the optimal features, which are later feed to SVM classifier for training and testing the DEAP dataset, where 32 participants with different gender and age groups are considered. The experimental results show that the proposed method exhibits 92.36% accuracy for our tested dataset. In addition, the proposed method outperforms than the state-of-art methods by exhibiting higher accuracy.

Handwritten digits recognition with decision tree classification: a machine learning approach

Abstract: Handwritten digits recognition is an area of machine learning, in which a machine is trained to identify handwritten digits. One method of achieving this is with decision tree classification model. A decision tree classification is a machine learning approach that uses the predefined labels from the past known sets to determine or predict the classes of the future data sets where the class labels are unknown. In this paper we have used the standard kaggle digits dataset for recognition of handwritten digits using a decision tree classification approach. And we have evaluated the accuracy of the model against each digit from 0 to 9.

Review of water-nanofluid based photovoltaic/thermal (PV/T) systems

Abstract: Solar energy is secure, clean, and available on earth throughout the year. The PV/T system is a device designed to receive solar energy and convert it into electric/thermal energy. Nanofluid is a new generation of heat transfer fluid with promising higher thermal conductivity and improve heat transfer rate compared with conventional fluids. In this review, the recent studies of PV/T using nanofluid is discussed regarding basic concept and theory PV/T, thermal conductivity of nanofluid and experimentally and theoretically study the perfromance of PV/T using nanofluid. A review of the literature shows that many studies have evaluated the potential of nanofluid as heat transfer fluid and optical filter in the PV/T system. The preparations of nanofluid play an essential key for high stability and homogenous nanofluid for a long period. The thermal conductivity of nanofluid is depending on the size of nanoparticles, concentration and preparation of nanofluids.

Emergent situations for smart cities: A survey

Abstract: A smart city is a community that uses communication and information technology to improve sustainability, livability, and feasibility. As any community, there are always unexpected emergencies, which must be treated to preserve the regular order. However, a smart system is needed to be able to respond effectively to these emergent situations. The contribution made in this survey is twofold. Firstly, it provides a comprehensive exhaustive and categorized overview of the existing surveys for smart cities. The categorization is based on several criteria such as structures, benefits, advantages, applications, challenges, issues, and future directions. Secondly, it aims to analyze several studies with respect to emergent situations and management to smart cities. The analysis is based on several factors such as the challenges and issues discussed, the solutions proposed, and opportunities for future research. The challenges include security, privacy, reliability, performance, scalability, heterogeneity, scheduling, resource management, and latency. Few studies have investigated the emergent situations of smart cities and despite the importance of latency factor for smart city applications, it is rarely discussed.

Simulation of various DC-DC converters for photovoltaic system

Abstract: This work explains the comparison of various dc-dc converters for photovoltaic systems. In recent day insufficient energy and continues increasing in fuel cost, exploration on renewable energy system becomes more essential. For high and medium power applications, high input source from renewable systems like photovoltaic and wind energy system turn into difficult one, which leads to increase of cost for installation process. So the generated voltage from PV system is boosted with help various boost converter depends on the applications. Here the various converters are like boost converter, buck converter, buck-boost converter, cuk converter, sepic converter and zeta converter are analysed for photovoltaic system, which are verified using matlab / simulink.

Detecting the magnitude of depression in Twitter users using sentiment analysis

Abstract: Today the different social networking sites have enabled everyone to easily express and share their feelings with people around the world. A lot of people use text for communicating, which can be done through different social media messaging platforms available today such as Twitter, Facebook etc, as they find it easier to express their feelings through text instead of speaking them out. Many people who also suffer from stress find it easier to express their feelings on online platform, as over there they can express themselves very easily. So if they are alerted beforehand, there are ways to overcome the mental problems and stress they are suffering from. Depression stands out to be one of the most well known mental health disorders and a major issue for medical and mental health practitioners. Legitimate checking can help in its discovery, which could be useful to anticipate and prevent depression all-together.Hence there is a need for a system, which can cater to such issues and help the user. The purpose of this paper is to propose an efficient method that can detect the level of depression in Twitter users. Sentiment scores calculated can be combined with different emotions to provide a better method to calculate depression scores. This process will help underscore various aspects of depression that have not been understood previously. The main aim is to provide a sense of understanding regarding depression levels in different users and how the scores can be correlated to the main data.

A new energy consumption technique for mobile ad hoc networks

Abstract: A dynamic temporary network is created through wireless mobile nodes without the need for considerable infrastructure as well as a central manager. In a mobile ad hoc network, routing protocols allow a mobile for transmission and receiving packets. In the last decade, many variants have come up for the AODV. A minimum number of hop counts are chosen for enhancing routing protocols to include additional factors that can have an impact on path selections. As the distance between each node grows, the transmission power also rises accordingly. Hence, this impacts the network’s entire performance and the most important feature is the quality of service. Most of the traditional routing protocols include energy consumption levels of the nodes and various parameters, like residual battery power, consumption of energy per packet and energy needed per transmission. A new technique is proposed in this paper to enhance the routing efficiency by making use of lion optimization algorithm after identifying all possible paths in the network. This technique not only enhances the energy efficiency of each node but also the performance metrics.

Arabic named entity recognition using deep learning approach

Abstract: Most of the Arabic Named Entity Recognition (NER) systems depend massively on external resources and handmade feature engineering to achieve state-of-the-art results. To overcome such limitations, we proposed, in this paper, to use deep learning approach to tackle the Arabic NER task. We introduced a neural network architecture based on bidirectional Long Short-Term Memory (LSTM) and Conditional Random Fields (CRF) and experimented with various commonly used hyperparameters to assess their effect on the overall performance of our system. Our model gets two sources of information about words as input: pre-trained word embeddings and character-based representations and eliminated the need for any task-specific knowledge or feature engineering. We obtained state-of-the-art result on the standard ANERcorp corpus with an F1 score of 90.6%.

Priority based round robin (PBRR) CPU scheduling algorithm

Abstract: This paper introduce a new approach for scheduling algorithms which aim to improve real time operating system CPU performance. This new approach of CPU Scheduling algorithm is based on the combination of round-robin (RR) and Priority based (PB) scheduling algorithms. This solution maintains the advantage of simple round robin scheduling algorithm, which is reducing starvation and integrates the advantage of priority scheduling. The proposed algorithm implements the concept of time quantum and assigning as well priority index to the processes. Existing round robin CPU scheduling algorithm cannot be dedicated to real time operating system due to their large waiting time, large response time, large turnaround time and less throughput. This new algorithm improves all the drawbacks of round robin CPU scheduling algorithm. In addition, this paper presents analysis comparing proposed algorithm with existing round robin scheduling algorithm focusing on average waiting time and average turnaround time.

Optimal power flow based congestion management using enhanced genetic algorithms

Abstract: Congestion management (CM) in the deregulated power systems is germane and of central importance to the power industry. In this paper, an optimal power flow (OPF) based CM approach is proposed whose objective is to minimize the absolute MW of rescheduling. The proposed optimization problem is solved with the objectives of total generation cost minimization and the total congestion cost minimization. In the centralized market clearing model, the sellers (i.e., the competitive generators) submit their incremental and decremental bid prices in a real-time balancing market. These can then be incorporated in the OPF problem to yield the incremental/ decremental change in the generator outputs. In the bilateral market model, every transaction contract will include a compensation price that the buyer-seller pair is willing to accept for its transaction to be curtailed. The modeling of bilateral transactions are equivalent to the modifying the power injections at seller and buyer buses. The proposed CM approach is solved by using the evolutionary based Enhanced Genetic Algorithms (EGA). IEEE 30 bus system is considered to show the effectiveness of proposed CM approach.

An IOT based smart metering development for energy management system

Abstract: The worldwide energy demand is increasing and hence necessity measures need to be taken to reduce the energy wastage with proper metering infrastructure in the buildings. A Smart meter can be used to monitor electricity consumption of customers in the smart grid technology. For allocating the available resources proper energy demand management is required. During the past years, various methods are being utilized for energy demand management to precisely calculate the requirements of energy that is yet to come. A large system presents a potential esteem to execute energy conservation as well as additional services linked to energy services, extended as a competent with end user is executed. The supervising system at the utilities determines the interface of devices with significant advantages, while the communication with the household is frequently proposing particular structures for appropriate buyer-oriented implementation of a smart meter network. Also, this paper concentrates on the estimation of vitality utilization. In this paper energy is measured in units and also product arrangement is given to create bill for energy consumption and implementing in LabVIEW software. An IOT based platform is created for remote monitoring of the metering infrastructure in the real time. The data visualization is also carried out in webpage and the data packet loss is investigated in the remote monitoring of the parameters.

Real time control and monitoring of grid power systems using cloud computing

Abstract: The use of grid power systems based on the combinations of various electrical networks, information technology, and communication layers called as Smart Grid systems. The technique of smart grid suppressed the problems faced by conventional grid systems such as inefficient energy management, improper control actions, grid faults, human errors, etc. The recent research on smart grid provides the approach for the real-time control and monitoring of grid power systems based on bidirectional communications. However, the smart grid is yet to improve regarding efficiency, energy management, reliability, and cost-effectiveness by considering its real-time implementation. In this paper, we present the real-time design of efficient monitoring and control of grid power system using the remote cloud server. We utilized the remote cloud server to fetch, monitor and control the real-time power system data to improve the universal control and response time. The proper hardware panel designed and fabricated to establish the connection with the grid as well as remote cloud users. The authenticated cloud users are provisioned to access and control the grid power system from anywhere securely. For the user authentication, we proposed the novel approach to secure the complete smart grid system. Finally, we demonstrated the effectiveness of real-time monitoring and control of the grid power method with the use of structure of practical framework.

A New Chaotic System with Line of Equilibria: Dynamics, Passive Control and Circuit Design

Abstract: A new chaotic system with line equilibrium is introduced in this paper. This system consists of five terms with two transcendental nonlinearities and two quadratic nonlinearities. Various tools of dynamical system such as phase portraits, Lyapunov exponents, Kaplan-Yorke dimension, bifurcation diagram and Poincare map are used. It is interesting that this system has a line of fixed points and can display chaotic attractors. Next, this paper discusses control using passive control method. One example is given to insure the theoretical analysis. Finally, for the new chaotic system, An electronic circuit for realizing the chaotic system has been implemented. The numerical simulation by using MATLAB 2010 and implementation of circuit simulations by using MultiSIM 10.0 have been performed in this study.

A comparative study of laravel and symfony PHP frameworks

Abstract: With the current explosion of Information Systems, the market offers a wide range of interesting technological solutions. Yet, this does not mean adopting a technology without considering its impact on the existing information system and user expectations. It is recommended to identify and implement the technological solutions most suited to the Information Systems strategy. Therefore, new methods are emerging and design tools are still evolving; the PHP Frameworks are part of it, which open up new perspectives in terms of information system enrichment. In this context, this paper focuses on the elaboration of a comparative study between Laravel, symfony framworks, which are the most popular PHP frameworks. Thus, it provides an effective comparison model that merges seven dimensions: Features, Multilingual, System requirements, Technical architecture, Code Organization, Continuous Integration (CI) and finally Documentation and learning curve dimension. Results show that our model can be beneficial for IT project developers to select the suitable PHP Framework.

A new model for the selection of web development frameworks: application to PHP frameworks

Abstract: The use of a framework is often essential for medium and large scale developments, but is also of interest for small developments. PHP has evolved as the scripting language the most chosen by developers, which has generated an explosion of PHP frameworks. There is a big debate about what the best PHP frameworks are, because the simple fact is that not all frameworks are built for everyone. Indeed, not all frameworks meet the same needs, and several frameworks can be used together in certain situations. Choosing the right framework, however, can sometimes be difficult. In order to make the selection process easier, we propose a pragmatic and complete model to compare and evaluate the main PHP frameworks. This model is based on a set of comparison criteria based on the Intrinsic durability, industrialized solution, technical adaptability, strategy, technical architecture and Speed criteria. Results show that the values of these criteria allow developers to easily and properly choose the framwork that best meets their needs

IoT based heart monitoring and alerting system with cloud computing and managing the traffic for an ambulance in India

Abstract: Global Burden of Disease Report, released in Sept 2017, shows that Cardio- vascular Diseases caused 1.7 million deaths (17.8%) in 2016 and it is the leading cause of deaths in India [1]. According to the Indian Heart Association, 25% of all heart attacks happen under the age of 40. In most cases, the initial heart attacks are often ignored. Even post-diagnosis, as per government data [2], 50% of heart attack cases reach the hospital in more than 400 minutes against the ideal window time of 180 minutes; post which damage is irreversible. The delay is often attributed to delay in reaching a hospital or receiving primary aid. In India, traffic conditions also add to the grimace of the situation. Although the government is taking various measures; a holistic solution is required to minimize the delay at each of the steps like accessing the patient situation, contacting the Medical aid or making available the nearest aid possible. In this paper, we aim at providing the holistic solution using the Internet of Things technology (IOT) along with data analytics. IoT enables real-time capturing and computation of medical data from smart sensors built-in wearable devices. The amalgamation of Internet-based services with Medical Things (Smart sensors) enhance the chances of survival of patients. The proposed system analyses the inputs collected from the sensors fit with the patients prone to cardiovascular diseases to ascertain the emergency situation. In addition, to these data, the system also considers age, maximum and minimum heart rate. Based on computational results received from the input parameters, the system triggers the alert to emergency contacts such as the close relatives of the patient, doctors, the hospitals and nearby ambulance. The proposed system combines with the optimized navigation platform to guide the medical assistance to find the fastest route.

Image watermarking based on integer wavelet transform-singular value decomposition with variance pixels

Abstract: With the era of rapid technology in multimedia, the copyright protection is very important to preserve an ownership of multimedia data. This paper proposes an image watermarking scheme based on Integer Wavelet Transform (IWT) and Singular Value Decomposition (SVD). The binary watermark is scrambled by Arnold transform before embedding watermark. Embedding locations are determined by using variance pixels. Selected blocks with the lowest variance pixels are transformed by IWT, thus the LL sub-band of 8×8 IWT is computed by using SVD. The orthogonal U matrix component of U3,1 and U4,1 are modified using certain rules by considering the watermark bits and an optimal threshold. This research reveals an optimal threshold value based on the trade-off between robustness and imperceptibility of watermarked image. In order to measure the watermarking performance, the proposed scheme is tested under various attacks. The experimental results indicate that our scheme achieves higher robustness than other scheme under different types of attack.

Fuzzy clustering and fuzzy c-means partition cluster analysis and validation studies on a subset of citescore dataset

Abstract: A hard partition clustering algorithm assigns equally distant points to one of the clusters, where each datum has the probability to appear in simultaneous assignment to further clusters. The fuzzy cluster analysis assigns membership coefficients of data points which are equidistant between two clusters so the information directs have a place toward in excess of one cluster in the meantime. For a subset of CiteScore dataset, fuzzy clustering (fanny) and fuzzy c-means (fcm) algorithms were implemented to study the data points that lie equally distant from each other. Before analysis, clusterability of the dataset was evaluated with Hopkins statistic which resulted in 0.4371, a value < 0.5, indicating that the data is highly clusterable. The optimal clusters were determined using NbClust package, where it is evidenced that 9 various indices proposed 3 cluster solutions as best clusters. Further, appropriate value of fuzziness parameter m was evaluated to determine the distribution of membership values with variation in m from 1 to 2. Coefficient of variation (CV), also known as relative variability was evaluated to study the spread of data. The time complexity of fuzzy clustering (fanny) and fuzzy c-means algorithms were evaluated by keeping data points constant and varying number of clusters.

A transient current based micro-grid connected power system protection scheme using wavelet approach

Abstract: Micro-grids comprise Distributed Energy Resources (DER’s) with low voltage distribution networks having controllable loads those can operate with different voltage levels are connected to the micro-grid and operated in grid mode or islanding mode in a coordinated way of control. DER’s provides clear environment-economical benefits for society and consumer utilities. But their development poses great technical challenges mainly protection of main and micro grid. Protection scheme must have to respond to both the main grid and micro-grid faults. If the fault is occurs on main grid, the response must isolate the DER’s from the main grid rapidly to protect the system loads. If the fault ocuurs within the micro-grid, the protection scheme must coordinate and isolates the least priority possible part of the grid to eliminate the fault. In order to deal with the bidirectional energy flow due to large numbers of micro sources new protection schemes are required. The system is simulated using MATLAB Wavelet Tool box and Wavelet based Multi-resolution Analysis is considered. Wavelet based Multi-resolution Analysis is used for detection, discrimination and location of faults on transmission network. This paper is discussed a transient current based micro-grid connected power system protection scheme using Wavelet Approach described on wavelet detailed-coefficients of Mother Biorthogonal 1.5 wavelet. The proposed algorithm is tested in micro-grid connected power systems environment and proved for the detection, discrimination and location of faults which is almost independent of fault impedance, fault inception angle (FIA) and fault distance of feeder line.

Review of solar photovoltaic/thermal (PV/T) air collector

Abstract: Growing concern with regard to energy sources and their usage has consequently increased significance of photovoltaic thermal (PV/T) collectors. A PV/T air collector is a system which has a conventional PV system combined with a thermal collector system. The system is able to produce electrical energy directly converted from sunlight by using photoelectric effect. Meanwhile, it also extracts heat from the PV and warms the fluid (air flow) inside the collector. In this review, solar PV system and solar thermal collectors are presented. In addition, studies conducted on solar PV/T air collectors are reviewed. The development of PV/T air collectors is a very promising area of research. PV/T air collectors using in solar drying and solar air heater.

An energy-efficient clustering protocol using fuzzy logic and network segmentation for heterogeneous WSN

Abstract: Wireless sensor networks have become an emerging research area due to their importance in the present industrial application. The enlargement of network lifetime is the major limitation in WSN. Several routing protocols study the extension of lifespan in WSN. Routing protocols significantly influence on the global of energy consumption for sensors in WSN. It is essential to correct the energy efficiency performance of routing protocol in order to improve the lifetime. The protocols based on clustering are the most routing protocols in WSN to reduce energy consumption. The protocols dedicate to WSN have demonstrated their limitation in expanding the lifetime of the network. In this paper, we present Hybrid SEP protocol : Multi-zonal Fuzzy logic heterogeneous Clustering based on Stable Election Protocol (FMZ-SEP). The FMZ-SEP characterizes by four parameters: WSN segmentation (splitting the WSN into the triangle zones ), the Subtractive Clustering Method to determine a correct number of clusters, the FCM and the SEP protocol. The FMZ-SEP prolong the stability period and extend the lifetime. The simulation results point out that the stability period of FMZ-SEP. FMZ-SEP protocol outperforms of MZ-SEP, FSEP and SEP protocol by improving the network lifetime and the stability period.

Tchebichef image watermarking along the edge using YCoCg-R color space for copyright protection

Abstract: Easy creation and manipulation of digital images present the potential danger of counterfeiting and forgery. Watermarking technique which embeds a watermark into the images can be used to overcome these problems and to provide copyright protection. Digital image watermarking should meet requirements, e.g. maintain image quality, difficult to remove the watermark, quality of watermark extraction, and applicable. This research proposes Tchebichef watermarking along the edge based on YCoCg-R color space. The embedding region is selected by considering the human visual characteristics (HVC) entropy. The selected blocks with minimum of HVC entropy values are transformed by Tchebichef moments. The locations of C(0,1), C(1,0), C(0,2) and C(2,0) of the matrix moment are randomly embedded for each watermark bit. The proposed watermarking scheme produces a good imperceptibility by average SSIM value around 0.98. The watermark recovery has greater resistant after several types of attack than other schemes.