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Security Solutions Using Brain Signals

10.11591/ijai.v7.i2.pp105-110
Anupama. H.S , Anusha M , Aparna Joshi , Apoorva N , N.K. Cauvery , Lingaraju. G.M
A Brain Computer Interface is a direct neural interface or a brain–machine interface. It provides a communication path between human brain and the computer system. It aims to convey people's intentions to the outside world directly from their thoughts. This paper focuses on current model which uses brain signals for the authentication of users. The Electro- Encephalogram (EEG) signals are recorded from the neuroheadset when a user is shown a key image (signature image). These signals are further processed and are interpreted to obtain the thought pattern of the user to match them to the stored password in the system. Even if other person is presented with the same key image it fails to authenticate as the cortical folds of the brain are unique to each human being just like a fingerprint or DNA.
Volume: 7
Issue: 2
Page: 105-110
Publish at: 2018-06-01

Diagnosis of Faulty Elements in Array Antenna using Nature Inspired Cuckoo Search Algorithm

10.11591/ijece.v8i3.pp1870-1874
Shafqat Ullah Khan , M. K. A. Rahim , Murtala Aminu-Baba , Atif Ellahi Khan Khalil , Sardar Ali
Detection and correction of faulty elements in a linear array have great importance in radar, sonar, mobile communications and satellite. Due to single element failure, the whole radiation pattern damage in terms of side lobes level and nulls. Once we have detect the position of defective element, then correction method is applied to achieve the desired pattern. In this work, we introduce a nature inspired meta-heuristic cuckoo search algorithm to diagnose the position of defective elements in a linear array. The nature inspired cuckoo search algorithm is new to the optimization family and is used first time for fault detection in an array antenna. Cuckoo search algorithm is a global search optimization technique. The cost function is used as a fitness function which defines an error between the degraded far field power pattern and the estimated one. The proposed technique is used effectively for the diagnosis of complete, as well as, for partial faulty elements position. Different simulation results are evaluated for 40 elements Taylor pattern to validate and check the performance of the proposed technique.
Volume: 8
Issue: 3
Page: 1870-1874
Publish at: 2018-06-01

Enhanced Three-Phase Inverter Faults Detection And Diagnosis Approach - Design And Experimental Evaluation

10.11591/ijpeds.v9.i2.pp559-570
Hicham Fadil , Mohamed Larbi Elhafyani , Smail Zouggar
Efficiency, reliability, high power quality and continuous operation are important aspects in electric vehicle attraction system. Therefore, quick fault detection, isolation and enhanced fault-tolerant control for open-switches faults in inverter driving systems become more and more required in this filed. However, fault detection and localization algorithms have been known to have many performance limitations due to speed variations such as wrong decision making of fault occurrence. Those weaknesses are investigated and solved in this paper using currents magnitudes fault indices, current direct component fault indices and a decision system. A simulation model and experimental setup are utilized to validate the proposed concept. Many simulation and experimental results are carried out to show the effectiveness of the proposed fault detection approach.
Volume: 9
Issue: 2
Page: 559-570
Publish at: 2018-06-01

CCCORE: Cloud Container for Collaborative Research

10.11591/ijece.v8i3.pp1659-1670
Salini Suresh , L. Manjunatha Rao
Cloud-based research collaboration platforms render scalable, secure and inventive environments that enabled academic and scientific researchers to share research data, applications and provide access to high- performance computing resources. Dynamic allocation of resources according to the unpredictable needs of applications used by researchers is a key challenge in collaborative research environments. We propose the design of Cloud Container based Collaborative Research (CCCORE) framework to address dynamic resource provisioning according to the variable workload of compute and data-intensive applications or analysis tools used by researchers. Our proposed approach relies on–demand, customized containerization and comprehensive assessment of resource requirements to achieve optimal resource allocation in a dynamic collaborative research environment. We propose algorithms for dynamic resource allocation problem in a collaborative research environment, which aim to minimize finish time, improve throughput and achieve optimal resource utilization by employing the underutilized residual resources.
Volume: 8
Issue: 3
Page: 1659-1670
Publish at: 2018-06-01

Comparison between PoW and PoS Systems Of Cryptocurrency

10.11591/ijeecs.v10.i3.pp1251-1256
Mohammad A. Al Ahmad , Abdullah Al-Saleh , Fahad A. Al Masoud
Cryptocurrency subject attracted so many people for the last eight years around the globe. Satoshi’s Nakamoto’s, the founder of the bitcoin cryptocurrency behind this revolutionary change in digital money market. Bitcoin cryptocurrency uses “Power of Work” or simply PoW system as its mining algorithm. But in January of 2016, Ethereum cryptocurrency has launched which adopted a new system called “Power of Stake” or simply PoS that is used in Ethereum as its mining algorithm. This paper explores and compares PoW and PoS systems that is used widely today in cryptocurrencies digital money, concluding the pros and cons for each system with enabling to decide which one is more suitable and stable in digital money market.
Volume: 10
Issue: 3
Page: 1251-1256
Publish at: 2018-06-01

An Experimental Investigation of Heating in Induction Motor under Open Phase Fault

10.11591/ijece.v8i3.pp1288-1296
Mahdi Atig , Mustapha Bouheraoua , Arezki Fekik
Although a three–phase squirrel cage induction motor is known by its qualities of robustness and low cost of construction. However, this machine can be affected by potential defects that affect the production, safety, quality of service and profitability of installations. However, to show the behavior of induction motor in different operating modes, the studying of this machine is very important. This paper presented the results of an experimental investigation to see the impact of the open phase fault on the thermal behavior in the 2.2 kW three phase squirrel cage induction motor, and to display the stator current waveforms with healthy and faulty conditions under different loads.
Volume: 8
Issue: 3
Page: 1288-1296
Publish at: 2018-06-01

Experimental Validation of Single Phase Series Active Power Filter Using Fuzzy Control Technique

10.11591/ijpeds.v9.i2.pp591-601
Abdallah Alla Ben Abdelkader , Othmane Abdelkhalek , Ahmed Allali , Abdelmalek Meftouhi , Toufik Toumi
The aim of this paper is illustrating and exposing the performance of a Series Active Power Filter (SAPF) with PI controller and Fuzzy controller within simulation and experimental validations. This performance is best manifested in compensating the sags and the swells voltages and in eliminating the harmonic voltage and regulate the terminal voltage of the load by injecting a voltage component in series with source voltage which is increased or decreased from the source voltage, hence, maintaining the load side waveforms as pure sinusoidal. The control method used in this work is not so complex and it is based on a phase locked loop (PLL) that is used to control the active filter. It is valid only in the phase system. The efficiency of the method I suggested is affirmed through simulation results by MATLAB\Simulink® and some prototypes experiments. These results shows the capability of the proposed prototypes
Volume: 9
Issue: 2
Page: 591-601
Publish at: 2018-06-01

Detecting and Shadows in the HSV Color Space using Dynamic Thresholds

10.11591/ijece.v8i3.pp1513-1521
Boutaina Hdioud , Mohammed El Haj Tirari , Rachid Oulad Haj Thami , Rdouan Faizi
The detection of moving objects in a video sequence is an essential step in almost all the systems of vision by computer. However, because of the dynamic change in natural scenes, the detection of movement becomes a more difficult task. In this work, we propose a new method for the detection moving objects that is robust to shadows, noise and illumination changes. For this purpose, the detection phase of the proposed method is an adaptation of the MOG approach where the foreground is extracted by considering the HSV color space. To allow the method not to take shadows into consideration during the detection process, we developed a new shade removal technique based on a dynamic thresholding of detected pixels of the foreground. The calculation model of the threshold is established by two statistical analysis tools that take into account the degree of the shadow in the scene and the robustness to noise.  Experiments undertaken on a set of video sequences showed that the method put forward provides better results compared to existing methods that are limited to using static thresholds.
Volume: 8
Issue: 3
Page: 1513-1521
Publish at: 2018-06-01

Single Phase Asymmetrical Cascaded MLI with Extreme Output Voltage Levels to Switch Ratio

10.11591/ijpeds.v9.i2.pp712-721
Mahrous Ahmed , Essam Hendawi , Mohamed K. Metwaly
This paper proposes an asymmetrical cascaded single phase H-bridge inverter. The proposed inverter consists of two modules with unequal and isolated dc sources. Each module is composed of dc source, conventional four switches H-bridge and single bidirectional switch. To increase the output voltage levels, the tertiary ratio, 1:3, between its two dc sources is adopted. Both the fundamental frequency and the multicarrier pulse width modulation (PWM) control schemes are employed to generate switches signals. By controlling the inverter modulation index, the proposed inverter can generate an output voltage having up to seventeen levels by using only two modules. The proposed topology has also the feature of modularity which means that it can be extended to any levels by adding new modules. The proposed topology is simulated using an inductive load and some selected simulation results have been provided to validate the proposed inverter.
Volume: 9
Issue: 2
Page: 712-721
Publish at: 2018-06-01

New Approaches in Cognitive Radios using Evolutionary Algorithms

10.11591/ijece.v8i3.pp1636-1646
Miguel Tuberquia , Cesar Hernandez
Cognitive radio has claimed a promising technology to exploit the spectrum in an ad hoc network. Due many techniques have become a topic of discussion on cognitive radios, the aim of this paper was developed a contemporary survey of evolutionary algorithms in Cognitive Radio. According to the art state, this work had been collected the essential contributions of cognitive radios with the particularity of base they research in evolutionary algorithms. The main idea was classified the evolutionary algorithms and showed their fundamental approaches. Moreover, this research will be exposed some of the current issues in cognitive radios and how the evolutionary algorithms will have been contributed. Therefore, current technologies have matters presented in optimization, learning, and classification over cognitive radios where evolutionary algorithms can be presented big approaches. With a more comprehensive and systematic understanding of evolutionary algorithms in cognitive radios, more research in this direction may be motivated and refined.
Volume: 8
Issue: 3
Page: 1636-1646
Publish at: 2018-06-01

Low Power CMOS Electrocardiogram Amplifier Design for Wearable Cardiac Screening

10.11591/ijece.v8i3.pp1830-1836
Ow Tze Weng , Suhaila Isaak , Yusmeeraz Yusof
The trend of health care screening devices in the world is increasingly towards the favor of portability and wearability. This is because these wearable screening devices are not restricting the patient’s freedom and daily activities. While the demand of low power and low cost biomedical system on chip is increasing in exponential way, the front-end electrocardiogram (ECG) amplifiers are still suffering from flicker noise for low frequency cardiac signal acquisition, 50Hz power line electromagnetic interference, and the large unstable input offsets due to the electrode-skin interface is not attached properly. In this paper, a CMOS based ECG amplifier that suitable for low power wearable cardiac screening is proposed. The amplifier adopts the highly stable folded cascode topology and later being implemented into RC feedback circuit for low frequency DC offset cancellation. By using  0.13µm CMOS technology from Silterra, the simulation results show that this front-end circuit can achieve a very low input referred noise of  1pV/Hz1/2 and high common mode rejection ratio of 174.05dB. It also gives voltage gain of 75.45dB with good power supply rejection ratio of 92.12dB. The total power consumption is only 3µW and thus suitable to be implemented with further signal processing and classification back end for low power wearable biomedical device.
Volume: 8
Issue: 3
Page: 1830-1836
Publish at: 2018-06-01

Evolutionary Computational Algorithm by Blending of PPCA and EP-Enhanced Supervised Classifier for Microarray Gene Expression Data

10.11591/ijai.v7.i2.pp95-104
Manaswini Pradhan
In DNA microarray technology, gene classification is considered to be difficult because the attributes of the data, are characterized by high dimensionality and small sample size. Classification of tissue samples in such high dimensional problems is a complicated task. Furthermore, there is a high redundancy in microarray data and several genes comprise inappropriate information for accurate classification of diseases or phenotypes. Consequently, an efficient classification technique is necessary to retrieve the gene information from the microarray experimental data. In this paper, a classification technique is proposed that classifies the microarray gene expression data well. In the proposed technique, the dimensionality of the gene expression dataset is reduced by Probabilistic PCA. Then, an Artificial Neural Network (ANN) is selected as the supervised classifier and it is enhanced using Evolutionary programming (EP) technique. The enhancement of the classifier is accomplished by optimizing the dimension of the ANN. The enhanced classifier is trained using the Back Propagation (BP) algorithm and so the BP error gets minimized. The well-trained ANN has the capacity of classifying the gene expression data to the associated classes. The proposed technique is evaluated by classification performance over the cancer classes, Acute myeloid leukemia (AML) and Acute Lymphoblastic Leukemia (ALL). The classification performance of the enhanced ANN classifier is compared over the existing ANN classifier and SVM classifier.
Volume: 7
Issue: 2
Page: 95-104
Publish at: 2018-06-01

Performance Analysis of Supercapacitor Integrated PV Fed Multistage Converter with SMC Controlled VSI for Different Load Conditions

10.11591/ijpeds.v9.i2.pp757-765
Shruti Pandey , Bharti Dwivedi , Anurag Tripathi
The proposed work comprises of an MPPT controlled Photovoltaic (PV) source, in conjunction with a supercapacitor, cascaded with a Sliding Mode Controlled (SMC) Inverter, supplying variable linear and nonlinear loads. The effects of varying solar irradiation and its intermittency have been effectively managed by the MPPT controlled boost converter and charge controlled supercapacitor respectively. The charge controller bucks and boosts the terminal voltage and realizes the power flow in a bidirectional manner. Seamless action has been obtained by the proposed model under varying irradiation and for varying load conditions. The performance of the SMC controlled Inverter, when compared with a PI controlled Inverter, has been found to be superior in terms of power quality and robustness of the supply system
Volume: 9
Issue: 2
Page: 757-765
Publish at: 2018-06-01

Optimization of Digital Histopathology Image Quality

10.11591/ijai.v7.i2.pp71-77
Furat N Tawfeeq , Nada A.S. Alwan , Basim M. Khashman
One of the biomedical image problems is the appearance of the bubbles in the slide that could occur when air passes through the slide during the preparation process. These bubbles may complicate the process of analysing the histopathological images. The objective of this study is to remove the bubble noise from the histopathology images, and then predict the tissues that underlie it using the fuzzy controller in cases of remote pathological diagnosis. Fuzzy logic uses the linguistic definition to recognize the relationship between the input and the activity, rather than using difficult numerical equation. Mainly there are five parts, starting with accepting the image, passing through removing the bubbles, and ending with predict the tissues. These were implemented by defining membership functions between colours range using MATLAB. Results: 50 histopathological images were tested on four types of membership functions (MF); the results show that (nine-triangular) MF get 75.4% correctly predicted pixels versus 69.1, 72.31 and 72% for (five- triangular), (five-Gaussian) and (nine-Gaussian) respectively. Conclusions: In line with the era of digitally driven e-pathology, this process is essentially recommended to ensure quality interpretation and analyses of the processed slides; thus overcoming relevant limitations.
Volume: 7
Issue: 2
Page: 71-77
Publish at: 2018-06-01

Data Analysis for Solar Energy Generation in a University Microgrid

10.11591/ijece.v8i3.pp1324-1330
Junghoon Lee , Seong Baeg Kim , Gyung-Leen Park
This paper presents a data acquisition process for solar energy generation and then analyzes the dynamics of its data stream, mainly employing open software solutions such as Python, MySQL, and R. For the sequence of hourly power generations during the period from January 2016 to March 2017, a variety of queries are issued to obtain the number of valid reports as well as the average, maximum, and total amount of electricity generation in 7 solar panels. The query result on all-time, monthly, and daily basis has found that the panel-by panel difference is not so significant in a university-scale microgrid, the maximum gap being 7.1% even in the exceptional case. In addition, for the time series of daily energy generations, we develop a neural network-based trace and prediction model. Due to the time lagging effect in forecasting, the average prediction error for the next hours or days reaches 27.6%. The data stream is still being accumulated and the accuracy will be enhanced by more intensive machine learning.
Volume: 8
Issue: 3
Page: 1324-1330
Publish at: 2018-06-01
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