Showing papers in "Indonesian Journal of Electrical Engineering and Informatics in 2019"

Network anomaly detection research: a survey

Abstract: Data analysis to identifying attacks/anomalies is a crucial task in anomaly detection and network anomaly detection itself is an important issue in network security. Researchers have developed methods and algorithms for the improvement of the anomaly detection system. At the same time, survey papers on anomaly detection researches are available. Nevertheless, this paper attempts to analyze futher and to provide alternative taxonomy on anomaly detection researches focusing on methods, types of anomalies, data repositories, outlier identity and the most used data type. In addition, this paper summarizes information on application network categories of the existing studies .

Self-adaptive fuzzy-PID controller for AGC study in deregulated Power System

Abstract: The aim of this paper elucidates the AGC issues in a large scale interconnected power system incorporating HVDC link under the deregulated environment. The performance of the system is degraded under the influence of abrupt load change, and parameter variation. To perceive a reliable and quality power supply, secondary robust controllers are essential. A novel self-adaptive Fuzzy-PID controller is proposed to ameliorate the dynamic performance of both the conventional PID and Fuzzy-PID controller, employed in the restructured power system. In self-adaptive Fuzzy-PID controller unlike the Fuzzy-PID controller, the output scaling factors are tuned dynamically while the controller is functioning. These three controllers are designed by enumerating different gains and scaling factors, applying a budding nature-inspired algorithm known as Wild Goat Algorithm (WGA). The superior dynamic performance of frequency and tie-line power deviation under self-adaptive Fuzzy-PID controller in comparison to its' counterparts is investigated by dispatching the scheduled and unscheduled power under different contracts such as poolco based transaction, bilateral transaction and contract violation based transaction through different tie-lines. The dynamic response under parameter variation and random load perturbation confers the robustness of the proposed controller.

Modified AES Cipher Round and Key Schedule

Abstract: In this paper, Advanced Encryption Standard was modified to address the lowdiffusion rate at the early rounds by adding additional primitive operationssuch as exclusive OR and modulo arithmetic in the cipher round. Furthermore,byte substitution and round constant addition were appended to the keyschedule algorithm. The modified AES was tested against the standard AESby means of avalanche effect and frequency test to measure the diffusion andconfusion characteristics respectively. The results of the avalanche effectevaluation show that there was an average increase in diffusion of 61.98% inround 1, 14.79% in round 2 and 13.87% in round 3. Consequently, the resultsof the frequency test demonstrated an improvement in the randomness of theciphertext since the average difference between the number of ones to zeros isreduced from 11.6 to 6.4 along with better-computed p-values. The resultsclearly show that the modified AES has improved diffusion and confusionproperties and the ciphertext can still be successfully decrypted and recoverback the original plaintext.

Sliding mode control for altitude and attitude stabilization of quadrotor UAV with external disturbance

Abstract: This paper addresses the problem of robust altitude and attitude control of ‘×’ mode configuration quadrotor UAV using Lyapunov stability based sliding mode control with saturation function. The dynamic model of the quadrotor was derived by considering nonlinearity factor. MATLAB Simulink was used to simulate the model in two different conditions; without and with the presence of external disturbance. This was done to test the robustness of the control method. Simulation results showed that the sliding mode controller provides good performance and robustness against disturbance.

An open source LoRa based vehicle tracking system

Abstract: This work describes an open source tracking system that determines the location and speed of a vehicle in real-time. The system was inspired by the need to track tourist boats in UNESCO Kilim Karst Geoforest Park, Malaysia. Boats that travel too fast generate wakes that are suspected to cause ecological damage. In this work, geolocation information is provided by Arduino based transponders with Global Positioning System (GPS). Transponders periodically transmit location and speed data using LoRa through a gateway to a cloud server. On the server, open source software components implement a Geographical Information System (GIS) to manage the location and speed data for display and further analysis. The resulting prototype performed the required functions as expected.

Design and Performance Analysis of 250 kW Grid-Connected Photovoltaic System in Iraqi Environment Using PVsyst Software

Abstract: A 250 kW grid-connected photovoltaic (PV) plant systems have been installed at the Ministry of Electricity in Baghdad and penetrated to the Iraqi national grid since November 2017. This is the first high power grid-connected PV system that has been installed in Iraq and it’s one of the four parts 1MW large-scale PV systems that should be completed in early of 2019. This paper presents the design and performance analysis of this system using a PVsyst software package. The performance ratio and different losses that occurred in the system are also calculated. The results show that the performance ratio is 75% using 1428 photovoltaic panels type (Sharp 175Wp) spread over an area of 1858 m². The total energy injected into the grid is (346692 kWh/year) .Based on the simulation results that developed in this paper, the practical PV grid-tied system has been implemented in Baghdad site.

Automatic Blood Vessel Extraction of Fundus Images Employing Fuzzy Approach

Abstract: Diabetic Retinopathy is a retinal vascular disease that is characterized by progressive deterioration of blood vessels in the retina and is distinguished by the appearance of different types of clinical lesions like microaneurysms, hemorrhages, exudates etc. Automated detection of the lesions plays significant role for early diagnosis by enabling medication for the treatment of severe eye diseases preventing visual loss. Extraction of blood vessels can facilitate ophthalmic services by automating computer aided screening of fundus images. This paper presents blood vessel extraction algorithms with ensemble of pre-processing and post-processing steps which enhance the image quality for better analysis of retinal images for automated detection. Extensive performance based evaluation of the proposed approaches is done over four databases on the basis of statistical parameters. Comparison of both blood vessel extraction techniques on different databases reveals that fuzzy based approach gives better results as compared to Kirsch’s based algorithm. The results obtained from this study reveal that 89% average accuracy is offered by the proposed MBVEKA and 98% for proposed BVEFA.

Frequency reconfigurable substrate integrated waveguide (SIW) cavity F-shaped slot antenna

Abstract: This paper shows a frequency reconfigurable SIW F-slot suitable for some applications. Configurability is accomplished by embeddings PIN diode switches in the 'F' slot. The proposed antenna is equipped for exchanging between working band of 3.172 GHz to 3.606 GHz in four different narrow bands and it underpins the cognitive system for LTE2300, WiMAX and WLAN. For each case reflection coefficient is ascertained, it keeps up less than -10 dB all through the resonating frequency in all instances of diode switching. The electromagnetic energy is kept inside the cavity because of the frame of a metallic vias. In all instances of diodes exchanging, it's uncovered a decent effectiveness of efficiency and gain.

Improving accuracy of derived 12-lead electrocardiography by waveform segmentation

Abstract: A number of methods have been proposed to reduce number of leads for electrocardiography (ECG) measurement without decreasing the signal quality. Some limited sets of leads that are nearly orthogonal, such as EASI, have been used to reconstruct the standard 12-lead ECG by various transformation techniques including linear, nonlinear, generic, and patient-specific. Those existing techniques, however, employed a full-cycle ECG waveform to calculate the transformation coefficients. Instead of calculating the transformation coefficients using a full-cycle waveform, we propose a new approach that segments the waveform into three segments: PR, QRS complex, and ST, hence the transformation coefficients were segment-specific. For testing, our new segment-specific approach was applied to six existing methods: Dower’s method with generic coefficients, Dower’s method with individual (patient-specific) coefficients, Linear Regression (LR), 2nd degree Polynomial Regression (PR), 3rd degree PR, and Artificial Neural Network (ANN). The results showed that the new approach outperformed the conventional full-cycle approach. It was able to significantly reduce the derivation error up to 74.50% as well as improve the correlation coefficient up to 0.66%.

Modeling and simulation of a wirelessly-powered thermopneumatic micropump for drug delivery applications

Abstract: This paper presents modeling and finite element analysis of a thermopneumatic micropump with a novel design that does not affect the temperature of the working fluid. The micropump is operated by activating a passive wireless heater using wireless power transfer when the magnetic field is tuned to match the resonant frequency of the heater. The heater is responsible for heating an air-heating chamber that is connected to a loading reservoir through a microdiffuser element. The solution inside the reservoir is pumped through a microchannel that ends with an outlet hole. The thermal and pumping performances of the micropump are analyzed using finite element method over a low range of Reynold’s number ⩽ 10 that is suitable for various biomedical applications. The results demonstrate promising performance with a maximum flow rate of ∼ 2.86 μL/min at a chamber temperature of 42.5 oC, and a maximum pumping pressure of 406.5 Pa. The results show that the developed device can be potentially implemented in various biomedical areas, such as implantable drug delivery applications.

Fusion Iris and Periocular Recognitions in Non-Cooperative Environment

Abstract: The performance of iris recognition in non-cooperative environment can be negatively impacted when the resolution of the iris images is low which results in failure to determine the eye center, limbic and pupillary boundary of the iris segmentation. Hence, a combination with periocular features is suggested to increase the authenticity of the recognition system. However, the texture feature of periocular can be easily affected by a background complication while the colour feature of periocular is still limited to spatial information and quantization effects. This happens due to different distances between the sensor and the subject during the iris acquisition stage as well as image size and orientation. The proposed method of periocular feature extraction consists of a combination of rotation invariant uniform local binary pattern to select the texture features and a method of color moment to select the color features. Besides, a hue-saturation-value channel is selected to avoid loss of discriminative information in the eye image. The proposed method which consists of combination between texture and colour features provides the highest accuracy for the periocular recognition with more than 71.5% for the UBIRIS.v2 dataset and 85.7% for the UBIPr dataset. For the fusion recognitions, the proposed method achieved the highest accuracy with more than 85.9% for the UBIRIS.v2 dataset and 89.7% for the UBIPr dataset.

Rain Attenuation at Tropical Region- Site Diversity Gain Models' Sensitivity

Abstract: Rain is the major impairment to the signal propagation from satellite to earth. The signal that brings an important data might be lost in a sudden due to heavy rainfall especially in the region with tropics climate. The undesirable effect mainly occurred at above of 10 GHz signal frequency which is expected to bring more data compared to a lower frequency. One of the possible solutions that was proven to be effective to overcome this impairment is the implementation of site diversity. The empirical model that has been developed to measure the effectiveness of the diverse site is yet to be finalized in tropical region. This article compares ITU-R, Hodge, Panagopoulos, Semire and Yeo Model to observe their sensitivity to the major factors that contributes to the site diversity gain such as frequency, site separation distance, elevation angle and baseline orientation angle. These major factors are used as input to the models. The default factors’ value was set to 20.2 GHz frequency, 68.8° of elevation angle, 42.52 km site separation distance and 65° of baseline angle. The factors were interchangeably with 12.255 GHz frequency, 25° of elevation angle, 10 km distance and 4° of baseline angle, which created 16 sets of combinations. The percentages of increment or decrement of the gain predicted by each model with respect to the default parameter were calculated. In overall, each model has their own discrepancy towards these factors and a more dynamic model should be developed to improve the weaknesses. Rain is the major impairment to the signal propagation from satellite to earth. The signal that brings an important data might be lost in a sudden due to heavy rainfall especially in the region with tropics climate. The undesirable effect mainly occurred at above of 10 GHz signal frequency which is expected to bring more data compared to a lower frequency. One of the possible solutions that was proven to be effective to overcome this impairment is the implementation of site diversity. The empirical model that has been developed to measure the effectiveness of the diverse site is yet to be finalized in tropical region. This article compares ITU-R, Hodge, Panagopoulos, Semire and Yeo Model to observe their sensitivity to the major factors that contributes to the site diversity gain such as frequency, site separation distance, elevation angle and baseline orientation angle. These major factors are used as input to the models. The default factors’ value was set to 20.2 GHz frequency, 68.8° of elevation angle, 42.52 km site separation distance and 65° of baseline angle. The factors were interchangeably with 12.255 GHz frequency, 25° of elevation angle, 10 km distance and 4° of baseline angle, which created 16 sets of combinations. The percentages of increment or decrement of the gain predicted by each model with respect to the default parameter were calculated. In overall, each model has their own discrepancy towards these factors and a more dynamic model should be developed to improve the weaknesses.

Recognition of Badminton Action Using Convolutional Neural Network

Abstract: Deep learning approach has becoming a research interest in action recognition application due to its ability to surpass the performance of conventional machine learning approaches. Convolutional Neural Network (CNN) is among the widely used architecture in most action recognition works. There are various models exist in CNN but no research has been done to analyse which model has the best performance in recognizing actions for badminton sport. Hence, in this paper we are comparing the performance of four different pre-trained models of deep CNN in classifying the badminton match images to recognize the different actions done by the athlete. Four models used for comparison are AlexNet, GoogleNet, VggNet-16 and VggNet-19. The images used in this experimental work are categorized into two classes: hit and non-hit action. Firstly, each image frame was extracted from Yonex All England Man Single Match 2017 broadcast video. Then, the image frames were fed as the input to each classifier model for classification. Finally, the performance of each classifier model was evaluated by plotting its performance accuracy in form of confusion matrix. The result shows that the GoogleNet model has the highest classification accuracy which is 87.5% compared to other models. In a conclusion, the pre-trained GoogleNet model is capable to be used in recognizing actions in badminton match which might be useful in badminton sport performance technology.

Improved time quantum length estimation for round robin scheduling algorithm using neural network

Abstract: In most cases, the quantum time length is taken to be fix in all applications that use Round Robin (RR) scheduling algorithm. Many attempts aim to determination of the optimal length of the quantum that results in a small average turnaround time, but the unknown nature of the tasks in the ready queue make the problem more complicated: Considering a large quantum length makes the RR algorithm behave like a First Come First Served (FIFO) scheduling algorithm, and a small quantum length cause high number of contexts switching. In this paper we propose a RR scheduling algorithm based on Neural Network Models for predicting the optimal quantum length which lead to a minimum average turnaround time. The quantum length depends on tasks burst times available in the ready queue. Rather than conventional traditional methods using fixed quantum length, this one giving better results by minimizing the average turnaround time for almost any set of jobs in the ready queue.

A secure arbiter physical unclonable functions (PUFs) for device authentication and identification

Abstract: Recent fourth industrial revolution, industry4.0 results in lot of automation of industrial processes and brings intelligence in many home appliances in the form of IoT, enhances M2M / D2D communication where electronic devices play a prominent role. It is very much necessary to ensure security of those devices. To provide reliable authentication and identification of each device and to abort the counterfeiting from the unauthorized foundries Physical Unclonable Functions (PUFs) emerged as a one of the promising cryptographic hardware security solution. PUF is function, mathematically modeled by using uncontrollable/ unavoidable random variances of the fabrication process of the ICs. These variances can generate unpredictable, random responses can be used to overcome the difficulties such as storing the keys in non-volatile memories (NVMs) in the classical cryptography. A wide variety of PUF architectures such as Arbiter PUFs, Ring oscillator PUFs, SRAM PUFs proposed by authors. But due to its design complexity and low cost, Delay based Arbiter PUFs (D-PUFs) are considering to be a one of the security primitives in authentication applications such as low-cost IoT devices for secure key generation. This paper presents a review on the different types of Delay based PUF architectures proposed by the various authors, sources to exhibit the physical disorders in ICs, methods to estimate the Performance metrics and applications of PUF in different domains.

A modified genetic algorithm with a new crossover mating scheme

Abstract: This study introduced the Inversed Bi-segmented Average Crossover (IBAX), a novel crossover operator that enhanced the offspring generation of the genetic algorithm (GA) for variable minimization and numerical optimization problems. An attempt to come up with a new mating scheme in generating new offspring under the crossover function through the novel IBAX operator has paved the way to a more efficient and optimized solution for variable minimization particularly on premature convergence problem using GA. A total of 597 records of student-respondents in the evaluation of the faculty instructional performance, represented by 30 variables, from the four State Universities and Colleges (SUC) in Caraga Region, Philippines were used as the dataset. The simulation results showed that the proposed modification on the Average Crossover (AX) of the genetic algorithm outperformed the genetic algorithm with the original AX operator. The GA with IBAX operator combined with rank-based selection function has removed 20 or 66.66% of the variables while 13 or 43.33% of the variables were removed when GA with AX operator and roulette wheel selection function was used.

Electrical Conductivity of Water Treated by Spark Discharge

Abstract: Electrical conductivity is an electrophysical characteristic, which is considered to be an estimate of the ability of substances to miss electrical current. Achieving the result of the formation of electrohydraulic discharges in water directly depends on the magnitude of the electrical conductivity, which argues the relevance of the topic. The paper presents the results of a study of the influence of electrohydraulic treatment on the change in the electrical conductivity of water. As a result of identifying significant factors influencing the process, rational parameters of the technological mode of electrohydraulic treatment of distilled, lake and tap water were selected. Curves of functional dependences of electrical conductivity on the number of spark high-voltage pulsed discharges are constructed. The relationship between the electrical conductivity of water and the modes of electrohydraulic effects is established.

Integration of time of use (TOU) tariff in net energy metering (NEM) scheme for electricity customers

Abstract: The introduction of Net Energy Metering (NEM) scheme for electricity customers in Malaysia is seen as an improvement from the previous Feed-In Tariff (FIT). However, the new NEM scheme only benefited the large residential customers but not to medium and small residential customers. Due to electricity tariff blocks structure, the large customers can avoid paying expensive tariff and hence reducing their electricity bill. This is not the case for medium and small customers since they are already paying lower tariff blocks due to their lower electricity consumption. This issue will discourage most residential customers to install solar PV system in their home and affect the Malaysia’s renewable energy target. This paper proposed a NEM scheme that integrates Time of Use (TOU) electricity tariff to the scheme. The proposed NEM-TOU scheme will be simulated, tested and compared to the new NEM scheme by using practical small, medium and large residential customers’ data. The results show that the proposed TOU-NEM scheme able to overcome the weakness of the current scheme where all customers (large, medium and low) can benefited by installing solar PV system in their home.

State of charge estimation based on adaptive algorithm for Lead-Acid battery

Abstract: The usage of batteries in recent years has become widespread in many fields e.g. in electric vehicles, energy renewable and stand-alone systems which require a robust approach for estimation of the state of charge (SOC). The SOC represents an important factor to guaranty safe operations. A lot of methods have been used to predict the state of charge. The coulomb counting method is the famous and widely used among them, but have limitation due to its accuracy. Another used approach is the Kalman Filter, which improves the estimation efficiency, to reach a good performance in SOC prediction. The version of adaptive extended Kalman filter (AEKF) technique is applied in this paper. This paper presents an experimental performance of technique of Kalman filter, for solving the problem of accurate SOC. The method is used to compute the terminal voltage in such a way to estimate the SOC. The proposed algorithm is based on preselected Thevenin model after the identification of its parameters. It has been used to predict the SOC based on nonlinear equations, and evaluation of the approach is verified with the experimental results. The final results signify that the estimation matched with the proposed model and the algorithm is performed optimally, thus the maximum soc estimation error is the finest

Influence of single vacancy defect at varying length on electronic properties of zigzag graphene nanoribbons

Abstract: Graphene, identified in 2004, is now an established two-dimensional (2D) material due to its outstanding physical and electronic characteristics namely its superior electrical conductivity. Graphene is a zero-gap material that has linear dispersion with electron-hole symmetry. As pristine sheet, it cannot be utilized in digital logic application without the induction of a band gap inside the band structure. In our work, the modeling and simulation of graphene nanoribbons (GNRs) are carried out to determine its electronics properties that are benchmarked with other published simulation data. A 4-Zigzag GNRs (4-ZGNRs) under different length are utilized. A single vacancy defects is introduced at various positions inside the atomic structure. The theoretical model is implemented based on single-neighbour tight binding technique coupled with a non-equilibrium Green’s function formalism. The single vacancy defects are represented by the elimination of tight binding energies in the Hamiltonian matrix. Subsequently, these matrix elements are utilized to compute dispersion relation and density of states (DOS) through Green’s function. It is found that single vacancy defects at different positions in 4-ZGNRs’ atomic structure under varying length has no significant impacts on the sub-band structure but these vacancies impact the DOS that are computed throught Green’s function approach.

The methods of duo output neural network ensemble for prediction of coronary heart disease

Abstract: The occurrence of Coronary heart disease (CHD) is hard to predict yet , but the assessment of CHD risk for the next ten years is possible . The p rediction of coronary heart disease can be modelled using multi-layer perceptron neural network (MLP-ANN). Prediction model with MLP-ANN ha s either positive or negative CHD output , which is a binary classification. A prediction model with binary classification requires determin ation of threshold value before the classification process which increases the uncertainty in the classification process. Another weakness of the MLP-ANN model is the presence of overfitting. This study propose s a prediction model for coronary heart disease using the duo output artificial neural network ensemble (DOANNE) method to overcome the problems of overfitting and uncertainty of classification in MLP-ANN. This research method wa s divided into several stages, namely data acquisition, pre-processing, modelling into DOANNE, neural network ensemble training with Levenberg-Marquard (LM) algorithm, system performance testing, and evaluation. The results of the study showed that the use of DOANNE-LM method was able to provide a significant improvement from the MLP-ANN method, indicated by the results of statistical tests with p-value <0.05.

Summative Stereoscopic Image Compression using Arithmetic Coding

Abstract: Image compression targets at plummeting the amount of bits required for image representation for save storage space and speed up the transmission over network. The reduction of size helps to store more images in the disk and take less transfer time in the data network. Stereoscopic image refers to a three dimensional (3D) image that is perceived by the human brain as the transformation of two images that is being sent to the left and right human eyes with distinct phases. However, storing of these images takes twice space than a single image and hence the motivation for this novel approach called Summative Stereoscopic Image Compression using Arithmetic Coding (S2ICAC) where the difference and average of these stereo pair images are calculated, quantized in the case of lossy approach and unquantized in the case of lossless approach, and arithmetic coding is applied. The experimental result analysis indicates that the proposed method achieves high compression ratio and high PSNR value. The proposed method is also compared with JPEG 2000 Position Based Coding Scheme(JPEG 2000 PBCS) and Stereoscopic Image Compression using Huffman Coding (SICHC). From the experimental analysis, it is observed that S2ICAC outperforms JPEG 2000 PBCS as well as SICHC.

A Bridge between Legacy Wireless Communication Systems and Internet of Things

Abstract: The software-defined radio (SDR) is a flexible platform that can adapt to various wireless telecommunication frequencies. It is able to provide a reconfigurable communication infrastructure for wireless systems. Hence, SDR is proposed here as a bridge between legacy wireless communication systems and the Internet of Things (IoT) via standard telecommunication protocols. The standard protocols are hypertext transfer protocol (HTTP), simple mail transfer protocol (SMTP), and message queuing telemetry transport (MQTT). Data collected from legacy wireless systems have been formatted via JavaScript object notation (JSON) for interoperability and categorized according to the application and the communication pattern. The extracted data are then transferred over MQTT for machine-to-machine (M2M) communication, over SMTP for machine-to-human (M2H) notification, and over HTTP for human-to-machine (H2M) communication. However, received audio signals from FM-based broadcasting stations have been transferred to the Internet servers over extensible messaging and presence protocol (XMPP), in live audio streaming. The objective is to introduce an SDR-IoT bridge that is inexpensive, scalable, and interoperable. The analyses show that the environment has good-performance, and can be used for many applications of smart city sectors, for Internet Radio, and for Internet-based monitoring of airplanes and vessel navigation.

Evaluation and Applying Feature Extraction Techniques for Face Detection and Recognition

Abstract: Detecting the image and identifying the face has become important in the field of computer vision for recognizing and analyzing, reconstructing into 3D, and labelling the image. Feature extraction is usually the first stage in detection and recognition of the image processing and computer vision. It supports the conversion of the image into a quantitative data. Later, this converted data can be used for labelling, classifying and recognizing a model. In this paper, performance of such feature extraction techniques viz. Local Binary Pattern (LBP), Histogram of Oriented Gradients (HOG) and Convolutional Neural Network (CNN) technique is applied to detect and recognize the face. The experiments conducted with a data set addressing the issues like pose variation, facial expression and intensity of light. The efficiency of the algorithms were evaluated based on the computational time and accuracy rate.

Fodder composition optimization using modified genetic algorithm

Abstract: Determination of the fodder composition is a difficult process because it should simultaneously consider several constraints, such as minimizing the total cost of feed ingredients and maximizing the nutrient needs required by livestock. This study uses a modified genetic algorithm to solve the problem in order to obtain better results. The modification is done by applying numerical methods in generating an initial population of the genetic algorithm. Testing results show that the optimal parameters that can be used to produce the optimal solution are as follows: population size (popsize) is 300, generation number is 400, crossover rate (cr) value is 0.2, and mutation rate (mr) value is 0.6. The modified genetic algorithm provides an average fitness value of 0.142357, while the classical genetic algorithm provides an average fitness value of 0.094354. With additional computational time equal to 110 ms, the use of modified genetic algorithm offered has proven to provide a better result, with a higher fitness value compared with classical genetic algorithm.

Control Strategy to Generate PWM Signals with Stability Analysis for Dual Input Power Converter System

Abstract: The prime role of a renewable resource based DC hybrid power system is, to maintain the output voltage constant with higher efficiency. In order to achieve this the duty cycles of the converter switches are dynamically controlled. Multiple input single output (MISO) converter uses separate controller for adjusting the duty cycle, this complicates the design and implementation of the system. Hence, to overcome this limitation a centralized controller is used. The control strategy depends on the pattern of gating signals given to the converter switches. When independent controller is employed, then gating signals of any pattern can be used to drive the switches. However, if a single controller is used, and then a definite pattern is very much essential otherwise, the output voltage and efficiency gets affected. In this paper, an attempt is made to validate and evaluate the performance parameters of MISO converter with two pattern of gating signals; they are synchronized and unsynchronized pulses at their rising edge. The control strategy focusses on the generation of these gating pulses. PID controller is tuned appropriately to determine the gains to achieve the stability of the proposed converter. The dual input power converter validated to show how the PWM pattern affects the efficiency, ripple and regulation of the converter. Using MATLAB SIMULINK platform the simulation of the proposed concept with dual input converter in closed loop is validated. Simulation results proves that synchronized pulses gives DC efficiency of 87% at designed output of 12V output. Converter with unsynchronized PWM pulses operates at lesser efficiency of 75% and the output voltage is of 10V.

Hard Exudate Extraction from Fundus Images using Watershed Transform

Abstract: Diabetic Retinopathy is a medical condition which affects the eyes due to increased blood sugar levels. This is characterized by presence of exudates - deposits of lipids in the posterior pole of the retina. If this ailment is not treated in earlier stages these deposits can cause blurred vision or even permanent blindness. This paper concentrates on extraction of hard exudates and optic disc from the retinal images of eyes using Marker based Watershed approach, which uses the minima imposition method to create mask and marker. The varying contrast across all the images has been taken care by a non-linear equation. Once these bright objects have been extracted from fundus images, area estimation is performed to eliminate the optic disk, thus retaining only exudates. These images have been procured from publicly available databases. Though software systems are easy to install, they prove to be expensive in terms of time and cost; thus this method has also been implemented on FPGA for an on-chip solution. The precision and sensitivity for exudate extraction sans optic disk are found to be 92.4% and 83.78% respectively. Though other techniques exist which provide better accuracy, the method described in this paper is found to be hardware friendly in comparison with other proven methods. Few steps of the algorithm developed are implemented on FPGA to provide an embedded system approach to this work, considering the advantages of a hardware-software combination.

Insights on Significant Implication on Research Approach for Enhancing 5G Network System

Abstract: With the exponential growth of mobile users, there is a massive growth of data as well as novel services to support such data management. However, the existing 4G network is absolutely not meant for catering up such higher demands of bandwidth utilization as well as servicing massive users with similar Quality of service. Such problems are claimed to be effectively addressed by the adoption of 5G networking system. Although the characteristics of 5G networking are theoretically sound, still it is under the roof of the research. Therefore, this paper presents a discussion about the conventional approach as well as an approach using cognitive radio network towards addressing the frequently identified problems of energy, resource allocation, and spectral efficiency. The study collects the existing, recent researches in the domain of 5G communications from various publications. Different from existing review work, the paper also contributes towards identifying the core research findings as well as a significant research gap towards improving the communication in the 5G network system.

Selecting Root Exploit Features Using Flying Animal-Inspired Decision

Abstract: Malware is an application that executes malicious activities to a computer system, including mobile devices. Root exploit brings more damages among all types of malware because it is able to run in stealthy mode. It compromises the nucleus of the operating system known as kernel to bypass the Android security mechanisms. Once it attacks and resides in the kernel, it is able to install other possible types of malware to the Android devices. In order to detect root exploit, it is important to investigate its features to assist machine learning to predict it accurately. This study proposes flying animal-inspired (1) bat, 2) firefly, and 3) bee) methods to search automatically the exclusive features, then utilizes these flying animal-inspired decision features to improve the machine learning prediction. Furthermore, a boosting method (Adaboost) boosts the multilayer perceptron (MLP) potential to a stronger classification. The evaluation jotted the best result is from bee search, which recorded 91.48 percent in accuracy, 82.2 percent in true positive rate, and 0.1 percent false positive rate.

Multi-Sensor System for Land and Forest Fire Detection Application in Peatland Area

Abstract: Forest fire has a dangerous impact on environments and humans because of haze and carbon emitted from it. A common technology to detect fire hotspots is to use satellite images and then process them to determine the number of hotspots and their location. However, satellite systems cannot penetrate in bad weather or cloudy condition. This research proposes a ground sensor system, which uses several sensors related to the indicators of fire, especially fire in peatland area with unique characteristics. Common parameters of fire, such as temperature, smoke, haze, and carbon dioxide, are applied in this system. Indicators are measured using special sensors. Results of every sensor are analyzed by implementing intelligent computer programming, and an algorithm to determine fire hotspots and locations is applied. The fire hotspot location and intensity determined by integrated multiple sensors are more accurate than those determined by a single sensor. Data collected from every sensor are kept in a database, and a graph is generated for reporting and recording. In case of sensor readings with parameters, potential of fire and hotspots detected can be forwarded to the representative department for corresponding actions.