Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
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Automatic Blood Vessel Extraction of Fundus Images Employing Fuzzy Approach
Get PDFDiabetic 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
Pattern of E-marketplace Customer Shopping Behavior using Tabu Search and FP-Growth Algorithm
No full textPattern of customer shopping behavior can be known by analyzing market cart. This analysis is performed using Association Rule Mining (ARM) method in order to improve cross-sale. The weakness of ARM is if processed data is big data, it takes more time to process the data. To optimize the ARM, we perform merging algorithm with Improved Tabu Search (TS). The application of Improved TS algorithm as optimization algorithm for preprocessing datasets, data filtering, and sorting data closely related products on sales data can optimize the ARM processing. The method of Association Rule Mining (FP-Growth) to determine frequent K-itemset, Support value and Confidence value of data which is already sorted on TS is based on patterns which often appear in the dataset so it generates rules as reference of decision making for company. To measure the level of power of rule which has been formed, the Lift Ratio value was calculated. Based on the calculation of 97 rules produced, the lift ratio produces values > 1 of 82.54% and based on processing time, it produces the fastest data search in 1.66 seconds. When compared with previous research that uses the hybrid method, for data retrieval based on processing time, it produces the fastest data search within 12.3406 seconds, 150 seconds and 50 seconds. Previous studies have only compared the processing time of data searching without regard to validation / accuracy of data search. The test results in this study obtained more optimal results than when compared with the results of previous studies, namely in time efficiency and data mining in real time and more accurate data validation. As a conclusion, the resulting rule can be used as a reference in understanding shopping behavior patterns customer on the E-Marketplace
Statcom integration in a power grid to enhance voltage stability
Get PDFTo solve load growth of an existing electrical system, we at first inject the power in system by building new electrical plants as wind, solar or thermal. And secondly in year 2030, when the system is so meshed, some customers are far away at production stations. We propose the installation of FACTS devices to enhance voltage in stability margins of power system under study. The power flow analysis using Newton Raphson method was running on Matlab and PSS/E Simulator. The results of simulation without STATCOM show the most affected buses. To improve the voltage for all these buses, reduce the power mismatches and tranmit more ative power, we insert STATCOM. STATCOM is a shunt connected FACTS application which facilitate the fast voltage control, the reactive power control and reduce the harmonics in a power system
Noise Cancellation Employing Adaptive Digital Filters for Mobile Applications
Get PDFThe persistent improvement of the hybrid adaptive algorithms and the swift growth of signal processing chip enhanced the performance of signal processing technique exalted mobile telecommunication systems. The proposed Artificial Neural Network Hybrid Back Propagation Adaptive Algorithm (ANNHBPAA) for mobile applications exploits relationship among the pure speech signal and noise corrupted signal in order to estimate of the noise. An adaptive linear system responds for changes in its environment as it is operating. Linear networks are gets adjusted at each time step based on new input and target vectors can find weights and biases that minimize the networks sum squared error for recent input and target vectors. Networks of this kind are quite oftenly used for error cancellation, speech signal processing and control systems. Noise in an audio signal has become major problem and hence mobile communication systems are demanding noise-free signal. In order to achieve noise-free signal various research communities have provided significant techniques. Adaptive noise cancellation (ANC) is a kind of technique which helps in estimation of un-wanted signal and removes them from corrupted signal. This paper introduces an Adaptive Filter Based Noise Cancellation System (AFNCS) that incorporates a hybrid back propagation learning for the adaptive noise cancellation in mobile applications. An extensive study has been made to explore the effects of different parameters, such as number of samples, number of filter coefficients, step size and noise level at the input on the performance of the adaptive noise cancelling system. The proposed hybrid algorithm consists all the significant features of Gradient Adaptive Lattice (GAL) and Least Mean Square (LMS) algorithms. The performance analysis of the method is performed by considering convergence complexity and bit error rate (BER) parameters along with performance analyzed with varying some parameters such as number of filter coefficients, step size, number of samples and input noise level. The outcomes suggest the errors are reduced significantly when the numbers of epochs are increased. Also incorporation of less hidden layers resulted in negligible computational delay along with effective utilization of memory. All the results have been obtained using computer simulations built on MATLAB platfor
Parameter Prediction for Lorenz Attractor by using Deep Neural Network
Get PDFNowadays, most modern deep learning models are based on artificial neural networks. This research presents Deep Neural Network to learn the database, which consists of high precision, a strange Lorenz attractor. Lorenz system is one of the simple chaotic systems, which is a nonlinear and characterized by an unstable dynamic behavior. The research aims to predict the parameter of a strange Lorenz attractor either yes or not. The primary method implemented in this paper is the Deep Neural Network by using Phyton Keras library. For the neural network, the different number of hidden layers are used to compare the accuracy of the system prediction. A set of data is used as the input of the neural network, while for the output part, the accuracy of prediction data is expected. As a result, the accuracy of the testing result shows that 100% correct prediction can be achieved when using the training data. Meanwhile, only 60% correct prediction is achieved for the new random data
Wideband Frequency Selective Surface Based Transmitarray Antenna at X-Band
Get PDFIn this paper, a wideband multilayer transmitarray antenna is designed for Ku frequency band. The unit cell is designed at 12GHz using frequency selective surface structure. Double square ring with center patch based multilayer unit cell is simulated. The effect of substrate thickness variation on transmission coefficient magnitude and phase range is discussed. The horn antenna designed at X-band will be used as feed source for transmitarray antenna. Transmitarray simulation results show wide impedance bandwidth from 10 to 13GHz. Wide gain bandwidth of 1.975GHz with peak gain of 18.96dB is achieved. The proposed transmitarray design will find applications in high gain, directional, low profile antennas for X-band communication systems
Characterization and Optimization of a Collinear Array of Circularly Polarized Side-Mounted Elements
Get PDFThis paper presents the radiation characteristics of a two-element collinear array of shunt-fed, slanted dipoles side-mounted to a metallic tower. It presents the radiation characteristics of the array as commonly implemented by FM broadcasters and suggests methods on how to optimize these characteristics such as the power gain, pattern circularity, side-lobe reduction, and horizontal-vertical polarization component ratio for the array to better conform to broadcast standards. By determining these characteristics using an advance antenna simulation software, optimization variables are identified, and their optimized values are determined. The optimized values lead to improved radiation characteristics of the array in conforming with broadcast requirements. Results show that adjusting the distance between the dipoles to less than one wavelength reduces the sidelobe of the array radiation pattern; varying the dipole physical specifications improves the polarization component ratio and the circularity of the radiation pattern. However, varying the dipole specifications also reduces the gain of the array. For applications requiring higher gains, other methods of realizing high collinear array gains are recommended
Student activities detection of SUST using YOLOv3 on Deep Learning
No full textThis article describes the main phases of a new learning system in which YOLOv3 is used for deep learning to identify student activities. A student’s exploits in the SUST- (Shaanxi University of Science and Technology) should be perceived to circumvent any unwanted problems. In this project, we have investigated the problem of image-based student activity detection in the SUST. It involves making a prediction by analyzing student poses, behavior, and activities with objects from complex images instead of videos. Comparing with all approaches, we conclusively decided to use an algorithm YOLOv3 (You Only Look Once) which is the latest and more convenient. The algorithm utilizes anchor boxes, bounding boxes, and a variant of Darknet. We have created our own dataset collecting images from SUST and annotated the dataset manually. During the research with this project, we have considered student activities in the SUST into seven sections namely reading, phoning, using a laptop, taking books, smiling, looking, and sleeping. The proposed system provides not only multi-tasking knowledge with classification but also localization of students and the equivalent actions instantaneously. Our intention is to detect the student position automatically, efficiently, confidently, and strictly with the help of extracted image functions. Interestingly, the proposed approach achieved a mean average precision (mAP) 97%. In the future, a combination of real-time data analysis will improve value to this scheme
Perceptual Image Quality Assessment Technique for Color Images based on HSI Colour Space
Get PDFA new full-reference Perceptual Image Quality Assessment (PIQA) technique based on Hue-Saturation-Intensity (HSI) transformation method for colour images is offered in this work. Fundamentally, it is combined with three approaches (colour transformation, histogram processing and Human Visual System (HVS) based weighting) and it uses the Discrete Cosine Transform (DCT) domain. The PIQA is composed of four phases. At the first phase, a colour image is transformed using the HSI because an important characteristic of eyes reaction to intensity of light and colour knowledge is used for quality assessment processes. All channels' DCT coefficients have been calculated at the second phase, because many specifications cannot be assessed in the spatial domain. At the third phase, histogram based quality assessment results are obtained by using histogram of each channel (Hue, Saturation and Intensity). These results are weighted for obtaining perceptual quality result taking into account the HVS specifications at the last phase because the human eye has different sensitivities to the intensity and the others (i.e., hue and saturation). Experimental outcomes about feasibility of the PIQA on test images under different deteriorations, both sensible by the HVS and with the same Peak Signal to Noise Ratio (PSNR) result are offered. The PIQA shows better performance in comparison to state-of-the-art technique
Impact of Electron Radiation Dose to the Performance of Half-Wave Rectifier and Converter Circuits with Silicon Carbide Schottky Diode
Get PDFHalf-wave rectifier; buck; and boost converter with electron-irradiated, high-voltage silicon carbide Schottky power diodes from CREE, Inc., performance were studied and presented in this paper subjected to electron radiation. The diodes were irradiated by high-energy (3 MeV) electrons with doses ranging from 1 to 5 MGy. The performance of the circuits in term of the output voltage were measured before and after the diodes being irradiated. It was observed, at 4 MGy, the half-wave rectifier output voltage degrades by 6.2 times as compared to before irradiation. Meanwhile, the output voltage of the buck converter degrades by 1.7 times; and for boost converter, the degradation of the output voltage is approximately 4.6 times for 4MGy radiation. These degradations are believed to be due to the increase in the series resistance of the Schottky diodes which is caused by the defects introduced inside the semiconductor during the irradiation and also the increase of turn-on voltage of the diodes after being irradiated