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31,456 Article Results

Optimization of electronic sensors for detecting pollution due to organic gases using PARAFAC

10.11591/ijece.v9i5.pp3441-3449
Shri Om Mishra , S Hasan Saeed
The principle point of this examination work is to recognize the butane, Acetone, Propane, ethane, LPG and other natural gases from the strong waste and do condition checking. Here the arrangement of sensors used to identify the poison gases from strong waste. Here our point is to build up a sensor cluster framework which will identify most extreme contamination gases and which is very responsive, minimal effort and low power devouring. We have assumed three sensors in position of six sensors and given the outcomes as fluctuation, score plot and stacking plot. Here we utilize the parallel factor analysis (PARAFAC) for identification of gases and contrast it and the key part investigation Principal component analysis (PCA). We confiscated three sensors in position of six sensors and given the outcomes as variance, score plot and loading plot. Electronic noses have given a plenty of advantages in different logical research fields. Here our point is to build up a sensor exhibit framework which will distinguish most extreme contamination gases and which is profoundly responsive, exact and minimal effort and low power expending. Here we utilize the parallel factor investigation method (PARAFAC) for discovery of gases and contrast it and the primary segment examination (PCA).
Volume: 9
Issue: 5
Page: 3441-3449
Publish at: 2019-10-01

Estimation of regression-based model with bulk noisy data

10.11591/ijece.v9i5.pp3649-3656
Chanintorn Jittawiriyanukoon
The bulk noise has been provoking a contributed data due to a communication network with a tremendously low signal to noise ratio. An appreciated method for revising massive noise of individuals through information theory is widely discussed. One of the practical applications of this approach for bulk noise estimation is analyzed using intelligent automation and machine learning tools, dealing the case of bulk noise existence or nonexistence. A regression-based model is employed for the investigation and experiment. Estimation for the practical case with bulk noisy datasets is proposed. The proposed method applies slice-and-dice technique to partition a body of datasets down into slighter portions so that it can be carried out. The average error, correlation, absolute error and mean square error are computed to validate the estimation. Results from massive online analysis will be verified with data collected in the following period. In many cases, the prediction with bulk noisy data through MOA simulation reveals Random Imputation minimizes the average error.
Volume: 9
Issue: 5
Page: 3649-3656
Publish at: 2019-10-01

An optimized method towards formal verification of mixed signals using differential fed neural network over FFNN

10.11591/ijece.v9i5.pp3423-3431
Vidya D.S. , Manjunath Ramachandra
Today, the semiconductor industries are rapidly usinganalog and mixed signals to achieve cost-effective solutions on a System on Chip (SoC) design.  The SoC device is a part of analog, digital and essential mixed-signal models/circuits merged on a semiconductor device, which provides the platform to build modern retail/consumer electronics appliances with smart technology. In order to evaluate the mixed signals, the conventional approaches are not effective with respect to its performance, time and manufacturing cost. Thus, the recent researches were much interested in formal verification technique as it provides the evidence of conscious algorithms in a system. The demand for formal verification in the SoC designs in the context of software and hardware platform is high because of its cost and accuracy. Thus, the paper introduces atechnique of formal verification for mixed signals by using training models of the Differential fed neural network (DFNN) over feedforward neural network (FFNN). The formal verification is performed through equivalence checking by using recently adopted designs as reference designs. The outcomes of the verification techniques suggests that DFNN based technique improves the training accuracy and optimizes the hardware resources like area, power than the FFNN based technique.
Volume: 9
Issue: 5
Page: 3423-3431
Publish at: 2019-10-01

Securing vehicular cloud networks

10.11591/ijece.v9i5.pp4154-4162
Djilali Moussaoui , Mohamed Feham , Boucif Amar Bensaber , Benamar Kadri
Vehicular Cloud Networks (VCN) is the network that ensures mobility and availability of resources allowing new services and applications like Network as a Service (NaaS), STorage as a Service (STaaS), Computation as a Service (CompaaS) and Cooperation as a Service (CaaS). In this paper, we propose a solution to secure the Vehicular Cloud Network (VCN). Our challenge in this work is to adapt the PKI architecture, which is mainly used in wired networks to be used in VCN. To propose a security solution for Vehicular Cloud Networks (VCN), our work is based on three steps; the first one is to make network architecture study, where we tried to highlight the main network components. The second step is to propose the security solution architecture. Finally, the last step is to program a test and validate the solution using a simulation.
Volume: 9
Issue: 5
Page: 4154-4162
Publish at: 2019-10-01

A hyprid technique for human footprint recognition

10.11591/ijece.v9i5.pp4060-4068
Yahya Ismail Ibrahim , Israa Mohammed Alhamdani
Biometrics has concerned a great care recently due to its important in the life that starts from civil applications to security and recently terrorism. A Footprint recognition is one of the personal identifications based on biometric measurements.  The aim of this research is to design a proper and reliable biometric system for human footprint recognition named (FRBS) that stands for Footprint Recognition Biometric System. In addition, to construct a human footprint database which it is very helpful for various use in scientific application e.g. for authentication. There exist many biometrics databases for other identity but very rare for footprint. As well as the existing one are very limited. This paper presents a robust hyprid techniques which merges between Image Processing with Artificial Intelligent technique via Ant Colony Optimization (ACO) to recognize human footprint.  (ACO) plays the essential role that rise the performance and the quality of the results in the biometric system via feature selection. The set of the selected features was treated as exploratory information, and selects the optimum feature set in standings of feature set size. Life RGB footprint images from nine persons with ten images per person constructed from life visual dataset. At first, the visual dataset was pre-processed operations. Each resultant image detects footprint that is cropped to portions represented by three blocks. The first block is for fingers, the second block refers to the center of the foot and the last one determines the heel. Then features were extracted from each image and stored in Excel file to be entered to Ant Colony Optimization Algorithm. The experimental outcomes of the system show that the proposed algorithm evaluates optimal results with smaller feature set comparing with other algorithms. Experimental outcomes show that our algorithm obtains an efficient and accurate result about 100% accuracy in comparison with other researches on the same field.
Volume: 9
Issue: 5
Page: 4060-4068
Publish at: 2019-10-01

A Shortest Data Window Algorithm for Detecting the Power Factor in presence of non-sinusoidal load current

10.11591/ijece.v9i5.pp3956-3966
Safaa S. Omran , Ali Sh. Al-Khalid , Amer Atta Yaseen
During recent years, nonlinear power electronic equipments introduce harmonic pollution on electric power systems. It makes the traditional power factor meter can not act accurately when it monitors unbalanced and harmonic loads. In this paper, a new algorithm for detecting the power factor in presence of non-sinusoidal load current is proposed. The proposed algorithm detects the true power factor exactly. By uses only two successive sampled data points of the voltage and the current for each displacement power factor value calculation and two sampled data points for each distortion power factor value calculation, the total/true power factor becomes easy to measure using these values directly. The proposed detector implemented using microcontroller as a main part and has been tested for single phase power system. The test results show that it can measure the true power factor of the loads quickly and accurately.
Volume: 9
Issue: 5
Page: 3956-3966
Publish at: 2019-10-01

Implementation of AES using biometric

10.11591/ijece.v9i5.pp4266-4276
Srividya R. , Ramesh B.
Mobile Adhoc network is the most advanced emerging technology in the field of wireless communication. MANETs mainly have the capacity of self-forming, self-healing, enabling peer to peer communication between the nodes, without relying on any centralized network architecture. MANETs are made applicable mainly to military applications, rescue operations and home networking. Practically, MANET could be attacked by several ways using multiple methods. Research on MANET emphasizes on data security issues, as the Adhoc network does not befit security mechanism associated with static networks. This paper focuses mainly on data security techniques incorporated in MANET. Also this paper proposes an implementation of Advanced Encryption Standard using biometric key for MANETs. AES implementation includes, the design of most robust Substitution-Box implementation which defines a nonlinear behavior and mitigates malicious attacks, with an extended security definition. The key for AES is generated using most reliable, robust and precise biometric processing. In this paper, the input message is encrypted by AES powered by secured nonlinear S-box using finger print biometric feature and is decrypted using the reverse process.
Volume: 9
Issue: 5
Page: 4266-4276
Publish at: 2019-10-01

Short-term optimal hydro-thermal scheduling using clustered adaptive teaching learning based optimization

10.11591/ijece.v9i5.pp3359-3365
Surender Reddy Salkuti
In this paper, Clustered Adaptive Teaching Learning Based Optimization (CATLBO) algorithm is proposed for determining the optimal hourly schedule of power generation in a hydro-thermal power system. In the proposed approach, a multi-reservoir cascaded hydro-electric system with a non-linear relationship between water discharge rate, net head and power generation is considered. Constraints such as power balance, water balance, reservoir volume limits and operation limits of hydro and thermal plants are considered. The feasibility and effectiveness of the proposed algorithm is demonstrated through a test system, and the results are compared with existing conventional and evolutionary algorithms. Simulation results reveals that the proposed CATLBO algorithm appears to be the best in terms of convergence speed and optimal cost compared with other techniques.
Volume: 9
Issue: 5
Page: 3359-3365
Publish at: 2019-10-01

Half-duplex power beacon-assisted energy harvesting relaying networks: system performance analysis

10.11591/ijece.v9i5.pp3399-3406
Tan N. Nguyen , Minh Tran , Van-Duc Phan , Hoang-Nam Nguyen , Thanh-Long Nguyen
In this work, the half-duplex (HF) power beacon-assisted (PB) energy harvesting (EH) relaying network, which consists of a source (S), Relay (R), destination (D) and a power beacon (PB) are introduced and investigated. Firstly, the analytical expressions of the system performance in term of outage probability (OP) and the system throughput (ST) are analyzed and derived in both amplify-and-forward (AF) and decode-and-forward (DF) modes. After that, we verify the correctness of the analytical analysis by using Monte-Carlo simulation in connection with the primary system parameters. From the numerical results, we can see that all the analytical and the simulation results are matched well with each other.
Volume: 9
Issue: 5
Page: 3399-3406
Publish at: 2019-10-01

The Channel Shortening comparison between OFDM & MC-CDMA System over an ADSL Channel using a Water-filling

10.11591/ijece.v9i5.pp3695-3700
Bouasria Fatima
In this paper our objective focuses on the development of digital transmission techniques in order to solve the problem of Inter-Symbol Interference (ISI) due to multiple paths and the improvement of Multi-Carrier Code Division Multiple Access (MC-CDMA) system performance. The both algorithm: MERRY algorithm (Multicarrier Equalization by Restoration of Redundancy) called TEQ (Time domain EQualizer) and the power allocation method to allocate appropriate power to each user (Water-filling technique (WF)) [1] is used. Finally, the numerical results show that combining Water Filling with TEQ technique allows to reduce the ISI for MC-CDMA systems.
Volume: 9
Issue: 5
Page: 3695-3700
Publish at: 2019-10-01

Video application to accommodate students’ learning style for moral education in teacher education institutes

10.11591/ijeecs.v16.i1.pp349-354
Mohd Razimi Bin Husin , Hishamuddin Bin Ahmad , Mahizer Bin Hamzah
The purpose of this study is to explore the favored video applications in video use, compatible techniques and technology to accommodate the learning styles of moral education students in Teacher Education Institutes. This study is also to explore the contents of lesson suit to technology preferred by students in learning Moral Education. This is a case study in where data were collected through interviews. Six semester four students were chosen through purposive sampling from two Teacher Education Institutes in Malaysia. The constructs and internal validity were verified by experts while the external validity was verified through support from existing studies and theories. Data of the study were analyzed by using the Nvivo software to identify the features of favored video, compatible techniques and contents of lesson suit to technology that enhance the quality of teaching and learning of Moral Education to students. The Physiological Stimulus of Dunn and Dunn Learning Styles Model was used as guidance in the interviews because this model was the most suitable to be applied to students with Physiological Stimulus. The results of the analysis showed that short videos with suitable musics and short duration are the favored video for moral education students in teacher education institutes that enabled students to memorize the content knowledge. This research could be the learning guidelines on the use of videos and consequences activities of the shown videos that accommodate the learning styles of moral education students in Teacher Education Institutes which could be used by instructors in their professional practice in the lecture room.
Volume: 16
Issue: 1
Page: 349-354
Publish at: 2019-10-01

Heartbeats: music recommendation system with fuzzy inference engine

10.11591/ijeecs.v16.i1.pp275-282
Vinothini Kasinathan , Aida Mustapha , Tan Sau Tong , Mohamad Firdaus Che Abdul Rani , Nor Azlina Abd Rahman
In developing a music recommendation system, there are several factors that can contribute to the inefficiency in music selection. One of the problems persists during the music listening is that common music playing application lacks the ability to acquire context of the user. Another problem that common music recommendation system fails to address the is emotional impact of the recommended song. To address this gap, this paper presents a music recommendation system based on fuzzy inference engine that considers user activities and emotion as part of the recommendation parameters. The system includes building a smart music recommendation system that has user profiling capabilities to recommend correct songs based on the user’s preferences, mood and time. Findings of the this paper have shown that Heartbeats’s fuzzy inference engine has successfully achieved its aim, which is to improve users’ music listening experience by giving suitable song recommendation based on user context situation.
Volume: 16
Issue: 1
Page: 275-282
Publish at: 2019-10-01

A new framework to alleviate DDoS vulnerabilities in cloud computing

10.11591/ijece.v9i5.pp4163-4175
A. Saravanan , S. SathyaBama , Seifedine Kadry , Lakshmana Kumar Ramasamy
In the communication age, the Internet has growing very fast and most industries rely on it. An essential part of Internet, Web applications like online booking, e-banking, online shopping, and e-learning plays a vital role in everyday life. Enhancements have been made in this domain, in which the web servers depend on cloud location for resources. Many organizations around the world change their operations and data storage from local to cloud platforms for many reasons especially the availability factor. Even though cloud computing is considered a renowned technology, it has many challenges, the most important one is security. One of the major issue in the cloud security is Distributed Denial of Service attack (DDoS), which results in serious loss if the attack is successful and left unnoticed. This paper focuses on preventing and detecting DDoS attacks in distributed and cloud environment. A new framework has been suggested to alleviate the DDoS attack and to provide availability of cloud resources to its users. The framework introduces three screening tests VISUALCOM, IMGCOM, and AD-IMGCOM to prevent the attack and two queues with certain constraints to detect the attack. The result of our framework shows an improvement and better outcomes and provides a recovered from attack detection with high availability rate. Also, the performance of the queuing model has been analysed.
Volume: 9
Issue: 5
Page: 4163-4175
Publish at: 2019-10-01

Real-time online fingerprint image classification using adaptive hybrid techniques

10.11591/ijece.v9i5.pp4372-4381
Annapurna Mishra , Satchidananda Dehuri
This paper presents three different hybrid classification techniques applied for the first time in real-time online fingerprint classification. Classification of online real time fingerprints is a complex task as it involves adaptation and tuning of classifier parameters for better classification accuracy. To accomplish the optimal adaptation of parameters of functional link artificial neural network (FLANN) for real-time online fingerprint classification, proven and established optimizers, such as Biogeography based optimizer (BBO), Genetic algorithm (GA), and Particle swarm optimizer (PSO) are intelligently infused with it to form hybrid classifiers. The global features of the real-time fingerprints are extracted using a Gabor filter-bank and then passed into adaptive hybrid classifiers for the desired classification as per the Henry system. Three hybrid classifiers, the optimized weight adapted Biogeography based optimized functional link artificial neural network (BBO-FLANN), Genetic algorithm based functional link artificial neural network (GA-FLANN) and Particle swarm optimized functional link artificial neural network (PSO-FLANN), are explored for real-time online fingerprint classification, where the PSO-FLANN technique  is showing superior performance as compared to GA-FLANN and BBO-FLANN techniques. The best accuracy observed by the application of PSO-FLANN, is 98% for real-time online fingerprint classification.
Volume: 9
Issue: 5
Page: 4372-4381
Publish at: 2019-10-01

K2SiF6:Mn4+ phosphor: recommendation for improving LO and D-CCT of the 7700K RPW-LEDs

10.11591/ijece.v9i5.pp3975-3980
Phu Tran Tin , Minh Tran , Van-Duc Phan , Hoang-Nam Nguyen , Thanh Trang Tran
In this paper, we propose a new recommendation for improving lumen output (LO) and CCT deviation (D-CCT) of the 7700 K RPW-LEDs by K2SiF6:Mn4+ red phosphor. Based on Light Tool and Mat Lab simulation software, we can see that the D-CCT and LO can be improved significantly, but the CQS and CRI have a slight decrease with increasing the concentration of the red phosphor. Besides, the analytical and simulation results agree well with each other.
Volume: 9
Issue: 5
Page: 3975-3980
Publish at: 2019-10-01
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