Showing papers in "International Journal of Electrical and Computer Engineering in 2018"

Unit Commitment Problem in Electrical Power System: A Literature Review

Abstract: Unit commitment (UC) is a popular problem in electric power system that aims at minimizing the total cost of power generation in a specific period, by defining an adequate scheduling of the generating units. The UC solution must respect many operational constraints. In the past half century, there was several researches treated the UC problem. Many works have proposed new formulations to the UC problem, others have offered several methodologies and techniques to solve the problem. This paper gives a literature review of UC problem, its mathematical formulation, methods for solving it and Different approaches developed for addressing renewable energy effects and uncertainties.

Performance Evaluation of Smart Home System using Internet of Things

Abstract: Nowadays, many researches have been conducted on smart home. Smart home control system (SHCS) can be integrated into an existing home appliances to reduce the need for human intervention, increase security and energy efficiency. We have proposed a smart home system using internet of things and four types of sensors, including PIR, temperature, ultrasonic, and smoke gas sensor for automatic environmental control and intrustion detection. In this paper, the performance of the previously developed prototype of smart home system will be evaluated. First, experiments on various sensors will be conducted. Next, the communicaton channel using wireless and Ethernet modules will be discussed. Moreover, the overall SHCS will be evaluated in terms of hardware and software performance. Additionaly, solar charger enhances the availability of our prototype system. Results showed the effectiveness of our proposed smart home system in the prototype and real life experiments.

Real-Time Heart Pulse Monitoring Technique Using Wireless Sensor Network and Mobile Application

Abstract: Wireless Sensor Networks (WSNs) for healthcare have emerged in the recent years. Wireless technology has been developed and used widely for different medical fields. This technology provides healthcare services for patients, especially who suffer from chronic diseases. Services such as catering continuous medical monitoring and get rid of disturbance caused by the sensor of instruments. Sensors are connected to a patient by wires and become bed-bound that less from the mobility of the patient. In this paper, proposed a real-time heart pulse monitoring system via conducted an electronic circuit architecture to measure Heart Pulse (HP) for patients and display heart pulse measuring via smartphone and computer over the network in real-time settings. In HP measuring application standpoint, using sensor technology to observe heart pulse by bringing the fingerprint to the sensor via used Arduino microcontroller with Ethernet shield to connect heart pulse circuit to the internet and send results to the web server and receive it anywhere. The proposed system provided the usability by the user (user-friendly) not only by the specialist. Also, it offered speed andresults accuracy, the highest availability with the user on an ongoing basis, and few cost.

Ant Colony Optimization (ACO) based Data Hiding in Image Complex Region

Abstract: This paper presents data an Ant colony optimization (ACO) based data hiding technique. ACO is used to detect complex region of cover image and afterward, least significant bits (LSB) substitution is used to hide secret information in the detected complex regions’ pixels. ACO is an algorithm developed inspired by the inborn manners of ant species. The ant leaves pheromone on the ground for searching food and provisions. The proposed ACO-based data hiding in complex region establishes an array of pheromone, also called pheromone matrix, which represents the complex region in sequence at each pixel position of the cover image. The pheromone matrix is developed according to the movements of ants, determined by local differences of the image element’s intensity. The least significant bits of complex region pixels are substituted with message bits, in order to hide secret information. The experimental results, provided, show the significance of the performance of the proposed method.

Music Emotion Classification based on Lyrics-Audio using Corpus based Emotion

Abstract: Music has lyrics and audio. That’s components can be a feature for music emotion classification. Lyric features were extracted from text data and audio features were extracted from audio signal data.In the classification of emotions, emotion corpus is required for lyrical feature extraction. Corpus Based Emotion (CBE) succeed to increase the value of F-Measure for emotion classification on text documents. The music document has an unstructured format compared with the article text document. So it requires good preprocessing and conversion process before classification process. We used MIREX Dataset for this research. Psycholinguistic and stylistic features were used as lyrics features. Psycholinguistic feature was a feature that related to the category of emotion. In this research, CBE used to support the extraction process of psycholinguistic feature. Stylistic features related with usage of unique words in the lyrics, e.g. ‘ooh’, ‘ah’, ‘yeah’, etc. Energy, temporal and spectrum features were extracted for audio features.The best test result for music emotion classification was the application of Random Forest methods for lyrics and audio features. The value of F-measure was 56.8%.

Gamification for Elementary Mathematics Learning in Indonesia

Abstract: The purpose of this research is to combine multimedia elements and mathematics learning material to a mathematic learning interactive application. Research and design methodology that used is Game Development Life Cycle (GDLC) which consist of initiation, pre-production, production, testing and release. Content inside the game is made using gamification and expert system concept. The result of this research is an interactive learning game to support student to understand mathematic materials. The purpose of this application is to help student to learn mathematic in an interactive and interesting way, to deliver mathematic material easily.

A Comparative Analysis on the Evaluation of Classification Algorithms in the Prediction of Diabetes

Abstract: Data mining techniques are applied in many applications as a standard procedure for analyzing the large volume of available data, extracting useful information and knowledge to support the major decision-making processes. Diabetes mellitus is a continuing, general, deadly syndrome occurring all around the world. It is characterized by hyperglycemia occurring due to abnormalities in insulin secretion which would in turn result in irregular rise of glucose level. In recent years, the impact of Diabetes mellitus has increased to a great extent especially in developing countries like India. This is mainly due to the irregularities in the food habits and life style. Thus, early diagnosis and classification of this deadly disease has become an active area of research in the last decade. Numerous clustering and classifications techniques are available in the literature to visualize temporal data to identify trends for controlling diabetes mellitus. This work presents an experimental study of several algorithms which classifies Diabetes Mellitus data effectively. The existing algorithms are analyzed thoroughly to identify their advantages and limitations. The performance assessment of the existing algorithms is carried out to determine the best approach.

Internet of Things: Low Cost and Wearable SpO2 Device for Health Monitoring

Abstract: This paper discusses a device for measuring oxygen saturation (SpO2) and heart rate as parameters of the representations of heart conditions. SpO2 device that have been made has a small dimension, wearable and high mobility with battery as the main power source. The device connects to a node MCU as a data processor and an internet network gateway to support internet of things applications. Data sent to the Internet cloud can be accessed online and real time via website for further analysis. The error rate at heart rate measurement is ± 2.8 BPM and for oxygen saturation (SpO2) is ± 1.5%. Testing data transmission delay until it can be displayed on website is 3 second that depends on internet traffic conditions.

Design and Implementation of Portable Outdoor Air Quality Measurement System using Arduino

Abstract: Recently, there is increasing public awareness of the real time air quality due to air pollution can cause severe effects to human health and environments. The Air Pollutant Index (API) in Malaysia is measured by Department of Environment (DOE) using stationary and expensive monitoring station called Continuous Air Quality Monitoring stations (CAQMs) that are only placed in areas that have high population densities and high industrial activities. Moreover, Malaysia did not include particulate matter with the size of less than 2.5μm (PM2.5) in the API measurement system. In this paper, we present a cost effective and portable air quality measurement system using Arduino Uno microcontroller and four low cost sensors. This device allows people to measure API in any place they want. It is capable to measure the concentration of carbon monoxide (CO), ground level ozone (O3) and particulate matters (PM10 & PM2.5) in the air and convert the readings to API value. This system has been tested by comparing the API measured from this device to the current API measured by DOE at several locations. Based on the results from the experiment, this air quality measurement system is proved to be reliable and efficient.

Solar Photovoltaic Power Forecasting in Jordan using Artificial Neural Networks

Abstract: In this paper, Artificial Neural Networks (ANNs) are used to study the correlations between solar irradiance and solar photovoltaic (PV) output power which can be used for the development of a real-time prediction model to predict the next day produced power. Solar irradiance records were measured by ASU weather station located on the campus of Applied Science Private University (ASU), Amman, Jordan and the solar PV power outputs were extracted from the installed 264KWp power plant at the university. Intensive training experiments were carried out on 19249 records of data to find the optimum NN configurations and the testing results show excellent overall performance in the prediction of next 24 hours output power in KW reaching a Root Mean Square Error (RMSE) value of 0.0721. This research shows that machine learning algorithms hold some promise for the prediction of power production based on various weather conditions and measures which help in the management of energy flows and the optimisation of integrating PV plants into power systems.

The Indonesia Public Information Disclosure Act (UU-KIP): Its Challenges and Responses

Abstract: Information Public Disclosure is one of the regulation that has purpose to encourage good governance for public service and citizen participation in national development. The enactment of Act No.14/2008 (UU KIP) has been strengthen the mandate to enforce the necessity of information disclosure in actualizing transparency and accountability in resource management and budget uses. It also become the primary instrument to prevent corruption, monopolistic competition and information disputes. However, there are certain provinces has not yet established information committee nor when it will be as entrusted by the regulation. Meanwhile, the remedies in term of jail duration and fines, arguably, it could not create deterrent effect to the perpetrator. Furthermore, the concern from ministry and public institution also in question in regard their roles of responsibility, lack of cooperation and continuous support. Thus, human resource, technology infrastructure, public participation, supervision and socialization become crucial factor to increase the awareness and satisfaction towards this regulatory compliance. This study is a qualitative research to evaluate the implementation of this Act by observing its consideration, background, principles and relevant article verses as primary sources through content analysis based on number of legal experts.

Load Balance in Data Center SDN Networks

Abstract: In the last two decades, networks had been changed according to the rapid changing in its requirements. The current Data Center Networks have large number of hosts (tens or thousands) with special needs of bandwidth as the cloud network and the multimedia content computing is increased. The conventional Data Center Networks (DCNs) are highlighted by the increased number of users and bandwidth requirements which in turn have many implementation limitations. The current networking devices with its control and forwarding planes coupling result in network architectures are not suitable for dynamic computing and storage needs. Software Defined networking (SDN) is introduced to change this notion of traditional networks by decoupling control and forwarding planes. So, due to the rapid increase in the number of applications, websites, storage space, and some of the network resources are being underutilized due to static routing mechanisms. To overcome these limitations, a Software Defined Network based Openflow Data Center network architecture is used to obtain better performance parameters and implementing traffic load balancing function. The load balancing distributes the traffic requests over the connected servers, to diminish network congestions, and reduce underutilization problem of servers. As a result, SDN is developed to afford more effective configuration, enhanced performance, and more flexibility to deal with huge network designs

Galvanic Skin Response Data Classification for Emotion Detection

Abstract: Emotion detection is a very exhausting job and needs a complicated process; moreover, these processes also require the proper data training and appropriate algorithm. The process involves the experimental research in psychological experiment and classification methods. This paper describes a method on detection emotion using Galvanic Skin Response (GSR) data. We used the Positive and Negative Affect Schedule (PANAS) method to get a good data training. Furthermore, Support Vector Machine and a correct preprocessing are performed to classify the GSR data. To validate the proposed approach, Receiver Operating Characteristic (ROC) curve, and accuracy measurement are used. Our method shows that the accuracy is about 75.65% while ROC is about 0.8019. It means that the emotion detection can be done satisfactorily and well performed.

Challenges and Opportunities of Internet of Things in Healthcare

Abstract: The Internet of Things (IoT) relies on physical objects interconnected between each other’s, creating a mesh of devices producing information and services. In this context, sensors and actuators are being continuously embedded in everyday objects (e.g., cars, home appliances, and smartphones) thus pervading our living environment. Among the plethora of application contexts, smart Healthcare is gaining momentum. Indeed IoT can revolutionize the healthcare industry by improving operational efficiency and clinical trials’ quality of monitoring, and by optimizing healthcare costs. This paper provides an overview of IoT, its applicability in healthcare, some insights about current trends and an outlook on future developments of healthcare systems.

Modeling and Simulation of Fuzzy Logic based Maximum Power Point Tracking (MPPT) for PV Application

Abstract: This paper presents modeling and simulation of maximum power point tracking (MPPT) used in solar PV power systems. The Fuzzy logic algorithm is used to minimize the error between the actual power and the estimated maximum power. The simulation model was developed and tested to investigate the effectiveness of the proposed MPPT controller. MATLAB Simulink was employed for simulation studies. The proposed system was simulated and tested successfully on a photovoltaic solar panel model. The Fuzzy logic algorithm succesfully tracking the MPPs and performs precise control under rapidly changing atmospheric conditions. Simulation results indicate the feasibility and improved functionality of the system.

Emotion Recognition from Facial Expression Based on Fiducial Points Detection and using Neural Network

Abstract: The importance of emotion recognition lies in the role that emotions play in our everyday lives. Emotions have a strong relationship with our behavior. Thence, automatic emotion recognition, is to equip the machine of this human ability to analyze, and to understand the human emotional state, in order to anticipate his intentions from facial expression. In this paper, a new approach is proposed to enhance accuracy of emotion recognition from facial expression, which is based on input features deducted only from fiducial points. The proposed approach consists firstly on extracting 1176 dynamic features from image sequences that represent the proportions of euclidean distances between facial fiducial points in the first frame, and faicial fiducial points in the last frame. Secondly, a feature selection method is used to select only the most relevant features from them. Finally, the selected features are presented to a Neural Network (NN) classifier to classify facial expression input into emotion. The proposed approach has achieved an emotion recognition accuracy of 99% on the CK+ database, 84.7% on the Oulu-CASIA VIS database, and 93.8% on the JAFFE database.

A Detail Study of Wavelet Families for EMG Pattern Recognition

Abstract: Wavelet transform (WT) has recently drawn the attention of the researchers due to its potential in electromyography (EMG) recognition system However, the optimal mother wavelet selection remains a challenge to the application of WT in EMG signal processing This paper presents a detail study for different mother wavelet function in discrete wavelet transform (DWT) and continuous wavelet transform (CWT) Additionally, the performance of different mother wavelet in DWT and CWT at different decomposition level and scale are also investigated The mean absolute value (MAV) and wavelength (WL) features are extracted from each CWT and reconstructed DWT wavelet coefficient A popular machine learning method, support vector machine (SVM) is employed to classify the different types of hand movements The results showed that the most suitable mother wavelet in CWT are Mexican hat and Symlet 6 at scale 16 and 32, respectively On the other hand, Symlet 4 and Daubechies 4 at the second decomposition level are found to be the optimal wavelet in DWT From the analysis, we deduced that Symlet 4 at the second decomposition level in DWT is the most suitable mother wavelet for accurate classification of EMG signals of different hand movements

Smart City Readiness based on Smart City Council’s Readiness Framework

Abstract: The level of urbanization which may impact on urban problems could be resolved through city development enabled and supported by the advanced ICT to build the city smart. To develop the city smart, the readiness of smart cities enablers should be assessed. The study was conducted based on pilot study through a survey on the smart city readiness. The analysis of smart city readiness in Yogyakarta showed that the evaluation of smart city projects implemented partially; only operational and asset optimization, and access to comprehensive device management implemented over 50%. Smart city readiness not only be measured by technological aspect but also need to be measured as non-technological aspects. Thus, measurement of readiness smart city can be more comprehensive.

A Simplified Design and Modeling of Boost Converter for Photovoltaic Sytem

Abstract: The Photovoltaic standalone system is gaining its high importance mostly for rural application like pv water pumping, solar lighting, battery charging etc.Considering environmental effects and scarcity of fossil fuel the trend has developed towards the use of more and more renewable energy.In this paper a basic circuit of boost converter is designed in MATLAB/Simulink with constant dc source voltage. However a comparative study has also been done for the converter connected with pv system directly with the converter connected with mppt tracking technique. Perturb and Observance (P&O) algorithm is implemented for providing the necessary duty pulse and makes the system operate at maximum power point.The boost converter connected with PV system without mppt operates at any other point other then the maximum power point and hence the output voltage decreases.But with mppt the proposed system performs better.

Feature Selection Mammogram based on Breast Cancer Mining

Abstract: The very dense breast of mammogram image makes the Radiologists often have difficulties in interpreting the mammography objectively and accurately. One of the key success factors of computer-aided diagnosis (CADx) system is the use of the right features. Therefore, this research emphasizes on the feature selection process by performing the data mining on the results of mammogram image feature extraction. There are two algorithms used to perform the mining, the decision tree and the rule induction. Furthermore, the selected features produced by the algorithms are tested using classification algorithms: k-nearest neighbors, decision tree, and naive bayesian with the scheme of 10-fold cross validation using stratified sampling way. There are five descriptors that are the best features and have contributed in determining the classification of benign and malignant lesions as follows: slice, integrated density, area fraction, model gray value, and center of mass. The best classification results based on the five features are generated by the decision tree algorithm with accuracy, sensitivity, specificity, FPR, and TPR of 93.18%; 87.5%; 3.89%; 6.33% and 92.11% respectively.

Forensic and Automatic Speaker Recognition System

Abstract: Current Automatic Speaker Recognition (ASR) System has emerged as an important medium of confirmation of identity in many businesses, ecommerce applications, forensics and law enforcement as well. Specialists trained in criminological recognition can play out this undertaking far superior by looking at an arrangement of acoustic, prosodic, and semantic attributes which has been referred to as structured listening. An algorithmbased system has been developed in the recognition of forensic speakers by physics scientists and forensic linguists to reduce the probability of a contextual bias or pre-centric understanding of a reference model with the validity of an unknown audio sample and any suspicious individual. Many researchers are continuing to develop automatic algorithms in signal processing and machine learning so that improving performance can effectively introduce the speaker’s identity, where the automatic system performs equally with the human audience. In this paper, I examine the literature about the identification of speakers by machines and humans, emphasizing the key technical speaker pattern emerging for the automatic technology in the last decade. I focus on many aspects of automatic speaker recognition (ASR) systems, including speaker-specific features, speaker models, standard assessment data sets, and performance metrics

Coordination of Adaptive Neuro Fuzzy Inference System (ANFIS) and Type-2 Fuzzy Logic System-Power System Stabilizer (T2FLS-PSS) to Improve a Large-scale Power System Stability

Abstract: Intelligent control included ANFIS and type-2 fuzzy (T2FLS) controllers grown-up rapidly and these controllers are applied successfully in power system control. Meanwhile, small signal stability problem appear in a large-scale power system (LSPS) due to load fluctuation. If this problem persists, and can not be solved, it will develop blackout on the LSPS. How to improve the LSPS stability due to load fluctuation is done in this research by coordinating of PSS based on ANFIS and T2FLS. The ANFIS parameters are obtained automatically by training process. Meanwhile, the T2FLS parameters are determined based on the knowledge that obtained from the ANFIS parameters. Input membership function (MF) of the ANFIS is 5 Gaussian MFs. On the other hand, input MF of the T2FLS is 3 Gaussian MFs. Results show that the T2FLS-PSS is able to maintain the stability by decreasing peak overshoot for rotor speed and angle. The T2FLS-PSS makes the settling time is shorter for rotor speed and angle on local mode oscillation as well as on inter-area oscillation than conventional/ ANFIS-PSS. Also, the T2FLS-PSS gives better performance than the other PSS when tested on single disturbance and multiple disturbances.

Selection of Digital Filter for Microprocessor Protection Relays

Abstract: The article considers some issues related to replacement of electromechanical relays used for protection of power facilities with microprocessor relays. One of the urgent problems connected with implementation of microprocessor overcurrent protections is how to use current transducers other than usual current transformers and in particular Rogowski coils that become more and more widespread. In the article are compared twelve methods of synthesis of a digital filter basing on the analog prototype – second-order integrating filter. The bilinear filter and Boxer-Thaler filters are analyzed in respect to their use in microprocessor relays. Basing on the research results a technique for selection of parameters of digital integrating filters for microprocessor relays is proposed. Simulation results show that Boxer-Thaler and bilinear filters have better accuracy during transient current measurements than the analog filter. The study allows concluding that in many cases the digital second-order bilinear filter is the best choice for use in microprocessor relays.

Exponential MLWDF (EXP-MLWDF) Downlink Scheduling Algorithm Evaluated in LTE for High Mobility and Dense Area Scenario

Abstract: Nowadays, with the advent of smartphones, most of people started to make voice and video conference calls continuously even in a high mobility scenario, the bandwidth requirements have increased considerably, which can cause network congestion phenomena. To avoid network congestion problems and to support high mobility scenario, 3GPP has developed a new cellular standard based packet switching, termed LTE (Long Term Evolution). The purpose of this paper is to evaluate the performance of the new proposed algorithm, named Exponential Modified Largest Weighted Delay First ‘EXP-MLWDF’, for high mobility scenario and with the presence of a large number of active users, in comparison with the well-known algorithms such as a proportional fair algorithm (PF), Exponential Proportional Fairness (EXP/PF), Logarithm Rule (LOG-Rule), Exponential Rule (EXP-Rule) and Modified Largest Weighted Delay First (MLWDF). The performance evaluation is conducted in terms of system throughput, delay and PLR. Finally, it will be concluded that the proposed scheduler satisfies the quality of service (QoS) requirements of the real-time traffic in terms of packet loss ratio (PLR), average throughput and packet delay. Because of the traffic evolution, some key issues related to scheduling strategies that will be considered in the future requirements are discussed in this article.

A New Chaotic System with a Pear-shaped Equilibrium and its Circuit Simulation

Abstract: This paper reports the finding a new chaotic system with a pear-shaped equilibrium curve and makes a valuable addition to existing chaotic systems with infinite equilibrium points in the literature. The new chaotic system has a total of five nonlinearities. Lyapunov exponents of the new chaotic system are studied for verifying chaos properties and phase portraits of the new system are unveiled. An electronic circuit simulation of the new chaotic system with pear-shaped equilibrium curve is shown using Multisim to check the model feasibility.

Unbalanced Voltages Impacts on the Energy Performance of Induction Motors

Abstract: This paper presents the results of a study about the effects of unbalanced voltages on the energy performance of three-phase induction motors. The principal contribution of this paper is that presents a detailed analysis of the influence of positive and negative sequence voltage components and the angle between them on several characteristics such as: line currents, losses, efficiency and power factor under different voltage unbalanced conditions. A three-phase induction motor of 3 HP was used as a case study. The results of the investigation show that the positive sequence voltage must be considered together with the voltage unbalance factor (VUF) or percent voltage unbalance (PVU) index to evaluate the performance of the induction motor. It is also shown that the behavior of the motor load influences on the positive sequence parameters next to the voltage, while in the case of negative sequence only influences the negative sequence voltage.

Pupil Detection Based on Color Difference and Circular Hough Transfor

Abstract: Human pupil eye detection is a significant stage in iris segmentation which is representing one of the most important steps in iris recognition. In this paper, we present a new method of highly accurate pupil detection. This method is consisting of many steps to detect the boundary of the pupil. First, the read eye image (R, G, B), then determine the work area which is consist of many steps to detect the boundary of the pupil. The determination of the work area contains many circles which are larger than pupil region. The work area is necessary to determine pupil region and neighborhood regions afterward the difference in color and intensity between pupil region and surrounding area is utilized, where the pupil region has color and intensity less than surrounding area. After the process of detecting pupil region many steps on the resulting image is applied in order to concentrate the pupil region and delete the others regions by using many methods such as dilation, erosion, canny filter, circle hough transforms to detect pupil region as well as apply optimization to choose the best circle that represents the pupil area. The proposed method is applied for images from palacky university, it achieves to 100 % accurac

A Self-Tuned Simulated Annealing Algorithm Using Hidden Markov Model

Abstract: Simulated Annealing algorithm (SA) is a well-known probabilistic heuristic. It mimics the annealing process in metallurgy to approximate the global minimum of an optimization problem. The SA has many parameters which need to be tuned manually when applied to a specific problem. The tuning may be difficult and time-consuming. This paper aims to overcome this difficulty by using a self-tuning approach based on a machine learning algorithm called Hidden Markov Model (HMM). The main idea is allowing the SA to adapt his own cooling law at each iteration, according to the search history. An experiment was performed on many benchmark functions to show the efficiency of this approach compared to the classical one.

A Novel Hybrid Classification Approach for Sentiment Analysis of Text Document

Abstract: Sentiment analysis is a more popular area of highly active research in Automatic Language Processing. She assigns a negative or positive polarity to one or more entities using different natural language processing tools and also predicted high and low performance of various sentiment classifiers. Our approach focuses on the analysis of feelings resulting from reviews of products using original text search techniques. These reviews can be classified as having a positive or negative feeling based on certain aspects in relation to a query based on terms. In this paper, we chose to use two automatic learning methods for classification: Support Vector Machines (SVM) and Random Forest, and we introduce a novel hybrid approach to identify product reviews offered by Amazon. This is useful for consumers who want to research the sentiment of products before purchase, or companies that want to monitor the public sentiment of their brands. The results summarize that the proposed method outperforms these individual classifiers in this amazon dataset.

Utilizing ECG Waveform Features as New Biometric Authentication Method

Abstract: In this study, we are proposing a practical way for human identification based on a new biometric method. The new method is built on the use of the electrocardiogram (ECG) signal waveform features, which are produced from the process of acquiring electrical activities of the heart by using electrodes placed on the body. This process is launched over a period of time by using a recording device to read and store the ECG signal. On the contrary of other biometrics method like voice, fingerprint and iris scan, ECG signal cannot be copied or manipulated. The first operation for our system is to record a portion of 30 seconds out of whole ECG signal of a certain user in order to register it as user template in the system. Then the system will take 7 to 9 seconds in authenticating the template using template matching techniques. 44 subjects‟ raw ECG data were downloaded from Physionet website repository. We used a template matching technique for the authentication process and Linear SVM algorithm for the classification task. The accuracy rate was 97.2% for the authentication process and 98.6% for the classification task; with false acceptance rate 1.21%.