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EMITTER International Journal of Engineering Technology
Published by Politeknik Elektronika Negeri Surabaya
ISSN : 2355391x     EISSN : -     DOI : -
Core Subject : Science,
Computer Science & IT
EMITTER International Journal of Engineering Technology is a BI-ANNUAL journal published by Politeknik Elektronika Negeri Surabaya (PENS). It aims to encourage initiatives, to share new ideas, and to publish high-quality articles in the field of engineering technology and available to everybody at no cost. It stimulates researchers to explore their ideas and enhance their innovations in the scientific publication on engineering technology. EMITTER International Journal of Engineering Technology primarily focuses on analyzing, applying, implementing and improving existing and emerging technologies and is aimed to the application of engineering principles and the implementation of technological advances for the benefit of humanity.
Arjuna Subject : -
Articles 436 Documents
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Analysis of control factors and surface integrity during wire-EDM of Inconel 718 alloy using T-GRA approach Md Ehsan Asgar; Ajay Kumar Singh Singholi
EMITTER International Journal of Engineering Technology Vol 9 No 2 (2021)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v9i2.633

Abstract

In today’s competitive modern manufacturing sectors, there is a vital need of utter precision and rigorous processing using various manufacturing approaches that directly influences the cost and processing duration of mechanized materials in addition to the consistency of the finished products. Therefore, it’s essential to figure out the required output by adjusting the control factors of any machining techniques which resulted in optimal values of the desired outcome. In this study, machining evaluation and process optimization is carried out on volumetric extraction of material namely material removal rate (MRR), kerf obtained during the machining (KW) and surface roughness (SR) of Inconel 718 superalloy during CNC controlled wire- electrical discharge machining. Four controllable factors- pulse interval, wire speed, pulse duration and peak current are considered to investigate the influence on performance measures. Taguchi's L16 has been used to construct the set of experiments before physical experimental runs and most influencing factors have been evaluated using ANOVA. SEM images and EDXS analysis have been resorted to examine the morphology of Inconel 718. These findings assist in identifying the topography of the machined surface. Further, the optimum integration has been obtained for the best yield and recorded using grey relational analysis integrated with Taguchi’s technique (T-GRA). The unfamiliarity of the work is based on consideration of zinc coated thin wire electrode and Taguchi-Grey combined approach of modelling with four levels of experimental design.
Plant disease prediction using convolutional neural network Hema M S; †, Niteesha Sharma; Y Sowjanya; Ch. Santoshini; R Sri Durga; V. Akhila
EMITTER International Journal of Engineering Technology Vol 9 No 2 (2021)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v9i2.640

Abstract

Every year India losses the significant amount of annual crop yield due to unidentified plant diseases. The traditional method of disease detection is manual examination by either farmers or experts, which may be time-consuming and inaccurate. It is proving infeasible for many small and medium-sized farms around the world. To mitigate this issue, computer aided disease recognition model is proposed. It uses leaf image classification with the help of deep convolutional networks. In this paper, VGG16 and Resnet34 CNN was proposed to detect the plant disease. It has three processing steps namely feature extraction, downsizing image and classification. In CNN, the convolutional layer extracts the feature from plant image. The pooling layer downsizing the image. The disease classification was done in dense layer. The proposed model can recognize 38 differing types of plant diseases out of 14 different plants with the power to differentiate plant leaves from their surroundings. The performance of VGG16 and Resnet34 was compared. The accuracy, sensitivity and specificity was taken as performance Metrix. It helps to give personalized recommendations to the farmers based on soil features, temperature and humidity
HActivityNet: A Deep Convolutional Neural Network for Human Activity Recognition Md. Khaliluzzaman; Md. Abu Bakar Siddiq Sayem; Lutful KaderMisbah
EMITTER International Journal of Engineering Technology Vol 9 No 2 (2021)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v9i2.642

Abstract

Human Activity Recognition (HAR), a vast area of a computer vision research, has gained standings in recent years due to its applications in various fields. As human activity has diversification in action, interaction, and it embraces a large amount of data and powerful computational resources, it is very difficult to recognize human activities from an image. In order to solve the computational cost and vanishing gradient problem, in this work, we have proposed a revised simple convolutional neural network (CNN) model named Human Activity Recognition Network (HActivityNet) that is automatically extract and learn features and recognize activities in a rapid, precise and consistent manner. To solve the problem of imbalanced positive and negative data, we have created two datasets, one is HARDataset1 dataset which is created by extracted image frames from KTH dataset, and another one is HARDataset2 dataset prepared from activity video frames performed by us. The comprehensive experiment shows that our model performs better with respect to the present state of the art models. The proposed model attains an accuracy of 99.5% on HARDatase1 and almost 100% on HARDataset2 dataset. The proposed model also performed well on real data.
Virtual Reality Technology and Speech Analysis for People Who Stutter Abeer Al-Nafjan; Najwa Alghamdi; Abdulaziz Almudhi
EMITTER International Journal of Engineering Technology Vol 9 No 2 (2021)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v9i2.649

Abstract

Virtual reality (VR) technology provides an interactive computer-generated experience that artificially simulates real-life situations by creating a virtual environment that looks real and stimulates the user’s feelings. During the past few years, the use of VR technology in clinical interventions for assessment, rehabilitation and treatment have received increased attention. Accordingly, many clinical studies and applications have been proposed in the field of mental health, including anxiety disorders. Stuttering is a speech disorder in which affected individuals have a problem with the flow of speech. This can manifest in the repetition and prolongation of words or phrases, as well as in involuntary silent pauses or blocks during which the individual is unable to produce sounds. Stuttering is often accompanied by a social anxiety disorder as a secondary symptom, which requires separate treatment. In this study, we evaluated the effectiveness of using a VR environment as a medium for presenting speech training tasks. In addition, we evaluated the accuracy of a speech analyzer module in detecting stuttering events.
Wavelet Transform and Convolutional Neural Network Based Techniques in Combating Sudden Cardiac Death Wanzita Shilla; Xiaopeng Wang
EMITTER International Journal of Engineering Technology Vol 9 No 2 (2021)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v9i2.663

Abstract

Sudden cardiac death (SCD) is a global threat that demands our attention and research. Statistics show that 50% of cardiac deaths are sudden cardiac death. Therefore, early cardiac arrhythmia detection may lead to timely and proper treatment, saving lives. We proposed a less complex, fast, and more efficient algorithm that quickly and accurately detects heart abnormalities. Firstly, we carefully examined 23 ECG signals of the patients who died from SCD to detect their arrhythmias. Then, we trained a deep learning model to auto-detect and distinguish the most lethal arrhythmias in SCD: Ventricular Tachycardia (VT) and Ventricular Fibrillation (VF), from Normal Sinus Rhythm (NSR). Our work combined two techniques: Wavelet Transform (WT) and pre-trained Convolutional Neural Network (CNN). WT was used to convert an ECG signal into scalogram and CNN for features extraction and arrhythmias classification. When examined in the MIT-BIH Normal Sinus Rhythm, MIT-BIH Malignant Ventricular Ectopy, and Creighton University Ventricular Tachyarrhythmia databases, the proposed methodology obtained an accuracy of 98.7% and an F-score of 0.9867, despite being less expensive and simple to execute.
Integrated Multi-view 3D Image Capture and Motion Parallax 3D Display System Madan Lal; Shadi Khan Baloch; Shoaib Rehman Soomro
EMITTER International Journal of Engineering Technology Vol 10 No 1 (2022)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v10i1.604

Abstract

We propose an integrated 3D image capture and display system using a transversely moving camera, regular 2D display screen and user tracking that can facilitate the multi-view capture of a real scene or object and display the captured perspective views in 3D. The motion parallax 3D technique is used to capture the depth information of the object and display the corresponding views to the user using head tracking. The system is composed of two parts, the first part consists of a horizontally moving camera interfaced with a customized camera control and capture application. The second part consist of a regular LCD screen combined with web camera and user tracking application. The 3D multi-view images captured through the imaging setup are relayed to the display based on the user location and corresponding view is dynamically displayed on the screen based on the viewing angle of the user with respect to the screen. The developed prototype system provides the multi-view capture of 60 views with the step size of 1 cm and greater than 40˚ field-of-view overlap. The display system relays 60 views providing the viewing angle coverage of ±35˚ where the angular difference between two views is 1.2˚.
Towards Improvement of LSTM and SVM Approach for Multiclass Fall Detection System Herti Miawarni; Eko Setijadi; Tri Arief Sardjono; Wijayanti; Mauridhi Hery Purnomo
EMITTER International Journal of Engineering Technology Vol 10 No 1 (2022)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v10i1.639

Abstract

Telemonitoring of human physiological data helps detect emergency occurrences for subsequent medical diagnosis in daily living environments. One of the fatal emergencies in falling incidents. The goal of this paper is to detect significant incidents such as falls. The fall detection system is essential for human body movement investigation for medical practitioners, researchers, and healthcare businesses. Accelerometers have been presented as a practical, low-cost, and dependable approach for detecting and predicting outpatient movements in the user. The accurate detection of body movements based on accelerometer data enables the creation of more dependable systems for incorporating long-term development in physiological remarks. This research describes an accelerometer-based platform for detecting users' body movement when they fall. The ADXL345, MMA8451q, and ITG3200 body sensors capture activity data, subsequently classified into 15 fall incident classes based on SisFall dataset. Falling incidents classification is performed using Long Short-Term Memory results in best AUC-ROC value of 97.7% and best calculation time of 6.16 seconds. Meanwhile, Support Vector Machines results in the best AUC-ROC value of 98.5% and best calculation times of 17.05 seconds.
An Improvement of Computer Based Test System Based on TCExam for Usage with A Large Number of Concurrent Users Yunarso Anang; Rahadi Jalu Yoga Utama; Masakazu Takahashi; Yoshimichi Watanabe
EMITTER International Journal of Engineering Technology Vol 10 No 1 (2022)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v10i1.667

Abstract

Computer-based test or assessment has been used widely, especially in the current COVID-19 pandemic, where many schools are conducting distance learning as well as distance examination. The need for a computer or software system to support education is inevitable. A range of solutions, from the free/open source software systems to the paid/proprietary ones have been publicly available. Still, an organization with limited resources prefers to find free or low-budget, while yet demanding reliable solutions. We have reported the use of the computer-based test in a new student recruitment test which is held country-wide. We developed the system based on TCExam, a free and open source computer-based test software, and successfully fulfilled the requirements, but with some tweaks. We found that the TCExam has a performance degradation when used by a large number of examinees concurrently, especially during specific phases during the test. This paper reports the result of our investigation to address the problem and suggests some modifications to the base codes as well as a recommendation of the hardware configuration. We evaluated the modified system in a simulated environment. We successfully achieved up to 56% performance gain using the modified system.
Automating Test Case Generation for Android Applications using Model-based Testing Usman Habib Khan; Muhammad Naeem Ahmed Khan; Aamir Mehmood Mirza; MUHAMMAD AKRAM; Shariqa Fakhar; Shumaila Hussain; Irfan Ahmed Magsi; Raja Asif Wagan
EMITTER International Journal of Engineering Technology Vol 10 No 1 (2022)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v10i1.628

Abstract

Testing of mobile applications (apps) has its quirks as numerous events are required to be tested. Mobile apps testing, being an evolving domain, carries certain challenges that should be accounted for in the overall testing process. Since smartphone apps are moderate in size so we consider that model-based testing (MBT) using state machines and statecharts could be a promising option for ensuring maximum coverage and completeness of test cases. Using model-based testing approach, we can automate the tedious phase of test case generation, which not only saves time of the overall testing process but also minimizes defects and ensures maximum test case coverage and completeness. In this paper, we explore and model the most critical modules of the mobile app for generating test cases to ascertain the efficiency and impact of using model-based testing. Test cases for the targeted model of the application under test were generated on a real device. The experimental results indicate that our framework reduced the time required to execute all the generated test cases by 50%. Experimental setup and results are reported herein.
Design Analysis of Array of Dipole Transmitters for Wireless Power Transfer Victor Adewuyi; Junior Milembolo Miantezila; Eunice Owoola
EMITTER International Journal of Engineering Technology Vol 10 No 1 (2022)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v10i1.638

Abstract

Considered in this work are the radiation aspects of a radio-frequency wireless power transfer system. Using the halfwave dipole as a candidate of choice, the current distribution on the antenna is first evaluated and presented using the versatile electromagnetic numerical Method of Moment technique (MoM). Using the current distribution obtained from the kernel of integration, the radiation fields for the single dipole element was obtained. Also, the analysis is extended to uniformly space linear antenna arrays using broadside and ordinary endfire arrays as candidates of interest. The simulation results for the broadside and endfire arrays were presented for 5, 6, 7, 10, 20 and 30 array elements at 0.3, 0.4 and 0.5 inter-element spacing. The peak directivity of broadside array occurs at 30 elements, 0.5λ spacing, and exceeds endfire array peak directivity by 11.27%. In addition to the advantage of an improved directivity achieved by the 7-element broadside array, an improved peak sidelobe level (PSLL) with the lowest PSLL for 7, 20, and 30 elements broadside array occurring at -12.0534 dB, -12.4298 dB, -12.6642 dB, -13.2246 dB, and -13.2747 dB respectively.

Page 38 of 44 | Total Record : 436

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