cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
-
Editorial Address
-
Location
,
INDONESIA
Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
Published by IAES Indonesia Section and Partners
ISSN : 20893272     EISSN : -     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) is a peer reviewed International Journal in English published four issues per year (March, June, September and December). The aim of Indonesian Journal of Electrical Engineering and Informatics (IJEEI) is to publish high-quality articles dedicated to all aspects of the latest outstanding developments in the field of electrical engineering. Its scope encompasses the engineering of Telecommunication and Information Technology, Applied Computing & Computer, Instrumentation & Control, Electrical (Power), Electronics, and Informatics.
Arjuna Subject : -
Articles 17 Documents
 1   2 
Search results for , issue "Vol 12, No 2: June 2024" : 17 Documents clear
Flexible Potentiostat Readout Circuit for Electrochemical Sensors Azmi, Nur Hanisah; Nordin, Anis Nurashikin; Suhaimi, Muhammad Irsyad; Ming, Lim Lai; Ab Rahim, Rosminazuin; Samsudin, Zambri; Md Ralib @ Md Raghib, Aliza Aini
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 2: June 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i2.5520

Abstract

Personalised health wearables reach their full potential when sensors are integrated with its interfacing system. Recent approaches have primarily focused on the development of readout circuits limited to the electrochemical chip and basic signal conditioning components. However, integrating a readout circuit with a microcontroller offers significant advantages such as enhanced data processing capabilities. Other than incorporating a microcontroller within the readout circuit, we also designed the entire potentiostat system on a flexible polyimide substrate, making it suitable for wearable applications. In this work, we describe the design, fabrication and testing of a flexible potentiostat readout circuit for electrochemical sensors. The core of the interface circuit is two chips, a microcontroller ATSAMD21G18A-MUT (Microchip Technology) and a programmable analog front-end integrated circuit from Texas Instruments. These chips along with a voltage regulator, resistors and capacitors were integrated onto a single, flexible, printed circuit board. To verify the functionality of the flexible readout circuit, it was connected to an electrochemical sensor and Cyclic Voltammetry (CV) was performed. The separation between peaks (ΔEp), were measured using the flexible board and compared with a commercial potentiostat (Emstat Pico). EmStat Pico has ΔEp = 0.133V, while our potentiostat produced ΔEp of 0.132V, indicating minimal variations with the same PCB layout, despite using different substrates. The standard rate constant (Ks) of electron transfer can also be obtained from CV and was measured to be 0.0037 for the rigid PCB and 0.0035 for the flexible PCB.
Trust-based Enhanced ACO Algorithm for Secure Routing in IoT Sharmin, Afsah; Motakabber, S. M. A.; Hashim, Aisha Hassan Abdalla
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 2: June 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i2.5118

Abstract

The Internet of Things (IoT) is an expanding paradigm of object connectivity using a range of resource types and architectures to deliver ubiquitous and requested services. There are security issues associated with the proliferation of IoT-connected devices, allowing IoT applications to evolve. In order to provide an energy-efficient and secure routing method for sensors deployed within a dynamic IoT network, this paper presents a trust-aware enhanced ant colony optimization (ACO)-based routing algorithm, incorporating a lightweight trust evaluation model. As it is challenging to implement security in resource-constrained IoT networks, the presented model adopted bioinspired approaches, offering an improved version of ACO towards secure data transmission cost-effectively while taking into consideration residual energy and the trust score of the sensor to be optimized. The trust evaluation system has been enhanced in the development of the proposed routing algorithm and the node trust value is evaluated, sensor node misbehavior is identified, and energy conservation is maximized. The performance evaluation is demonstrated utilizing MATLAB. In comparison to the standard bioinspired algorithms and existing secure routing protocols, the proposed system reduces average energy consumption by nearly 50% regardless of the increase in the number of nodes and end-to-end delay of 40%, while finding the secure and optimal path in unison is designed to ensure trust in the IoT environment.
Bengali Word Detection from Lip Movements Using Mask RCNN and Generalized Linear Model Bhuiyan, Abul Bashar; Uddin, Jia
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 2: June 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i2.5088

Abstract

Speech processing with the help of lip detection and lip reading is an advancing field. For this, we need proper algorithms and techniques to detect lips and movements of lips perfectly. Lip detection and configuration are the most important parts of speech recognition. In this paper, we focus on detecting the lip segment properly. Mask R-CNN (Regional Convolutional Neural Network) performs object detection and instance segmentation per video frame to detect the lip segment. The process of mask R-CNN adds only a small overhead to Faster R-CNN and is quite simple to train, running at 5 frames per second. The Mask R-CNN involves keypoint detection which helps to extract the location of the lip landmarks pixel by pixel. Once the lip region is extracted and the landmarks are highlighted, we observe how the lip landmarks change as the object's lips move over time to each Bengali word. The keypoint changes that are observed during each millisecond are then the landmarks used to train the GLM (Generalized Linear Model). In addition, we compare the performance of GLM with Naive Bayes, Logistic Regression, and Decision Tree. The GLM has exhibited the highest 91.8% accuracy, whereas the Naive Bayes, Logistic Regression, and Decision Tree show the accuracy of 87.1%, 38.3%, and 82.2%, respectively.
DR-CNN+ Approach for Standardized Diabetic Retinopathy Severity Assessment Majid, Samiya; Bala, Indu
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 2: June 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i2.4890

Abstract

Diabetic retinopathy (DR) is a serious eye disorder that damages the retina and can lead to vision impairment and blindness, especially in individuals with diabetes. Early identification is crucial for a positive outcome, however, diabetic retinopathy can only be diagnosed with color fundus photographs, which is a technique that is difficult and time-consuming. To address this issue, this paper presents a Deep Learning-based algorithm that utilizes DR - convolutional neural network+ (DR-CNN+) to classify retinal pictures into different stages of diabetic retinopathy. The proposed algorithm is trained on a dataset of 11000 colored retinal pictures from the training set and 2200 photos from the testing set. The simulation results demonstrate that the DRCNN+-based algorithm can achieve high levels of accuracy, sensitivity, and specificity. Our proposed DR-CNN+ model not only improves diagnostic performance for diabetic retinopathy severity evaluation, but it also saves training time by 95% when compared to current models." Overall, this paper highlights the potential of using deep learning and CNNs to improve the detection and grading of diabetic retinopathy, which could have a significant impact on the prevention of blindness caused by this disease.
A Compact Violin-Shaped Monopole Antenna for Ultra-Wideband Applications Alwan, Younes S.; Zidan, Mohammad S.; Ibrahim, Omar J.
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 2: June 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i2.5404

Abstract

A case study of a miniature monopole planar fiddle or violin-shaped antenna that can be used in ultra-wideband (UWB) application is carried out. Such violin-shaped antenna is a circular patch accompanied with two circular cuts and overlapped with an elliptical patch on the top of it. It is small in size, simple in structure, feasible to construct and experimentally feasible to be manufactured and validated in lab. Furthermore, the handle of the fiddlelike structure serves as a microstrip 50? feeding line connected to the main patch structure body. However, the prototype of the designed antenna is manufactured on a substrate of a dielectric material of FR4 with a dielectric constant that equals 4.3 with dimensions of 28×18×1.6 mm3. The gain at the resonant frequencies reached different values throughout the covered frequency band; that is of (3.1 GHz up to 13.5 GHz) ranging between the values of (≈1.1 dBi up to ≈5.5 dBi) according to the return loss of the performance outcome. The empirically measured and simulated results have a suitable settlement and/or agreement and computations display that the antenna has a respectable frequency band, radiation, and characteristics of time domain in spite of the antenna’s small size and simple design.
Advanced Techniques for Improved Bangladeshi Number Plate Detection and Character Recognition in Automated Parking Systems Khan, Niaz Ashraf; Bin Hafiz, Md. Ferdous
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 2: June 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i2.5477

Abstract

This paper presents a novel technique for efficient extraction of vehicle number plates from camera-captured images and accurate recognition of Bangla characters embedded within them. With the exponential growth of vehicular traffic in densely populated regions like Bangladesh, automation becomes crucial, making vehicle plate recognition pivotal for tracking stolen vehicles and enhancing traffic control measures. Leveraging conventional computer vision and image processing techniques, our proposed system incorporates specific features inherent to Bangladeshi number plates, thus enhancing recognition accuracy. Our application makes use of the OpenCV library to underscore the strength of the algorithm, which has been confirmed through real-time testing across different weather conditions and varying image qualities. The results show a remarkable accuracy rate of 92.3%, affirming our technique's reliability in vehicle number plate detection and character recognition. Moreover, the integration with MySQL database and Arduino UNO enables real-time application in automated parking systems, offering seamless entry procedures and accurate billing, thus addressing critical concerns in modern transportation management systems. Our algorithm not only enhances security measures but also streamlines parking facility management, contributing to safer and more efficient urban mobility solutions.
Semantic Similarity Measure Using a Combination of Word2Vec and WordNet Models Fellah, Aissa; Zahaf, Ahmed; Elçi, Atilla
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 2: June 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i2.5114

Abstract

The cognitive effort required for humans to perceive similarities and relationships between words is considerable. Measuring similarity and relatedness between text components such as words, texts, or documents is challenging, and it continues to be an active area of research across various domains. The complexity of language and the diverse factors that influence similarity and relatedness make this task an ongoing research focus. Researchers are exploring diverse approaches, to improve the accuracy and effectiveness of measuring similarity and relatedness in text. The utilization of knowledge sources, such as WordNet, has been a popular approach for modeling semantic relationships between words. However, Recently, distributional semantic models, such as Word2Vec, have demonstrated their ability to effectively capture semantic information and outperform lexiconbased methods in terms of unidirectional contextual similarity outcomes. In contrast to lexicon-based approaches, which rely on structure, distributional models leverage context to capture semantics. This study proposes a novel approach that linearly combines the lexical databases WordNet and Word2Vec to measure semantic similarity, focusing on improving upon previous techniques. The proposed approach is thoroughly detailed and evaluated using popular datasets to determine its effectiveness. The experimental results indicate that the proposed approach achieves highly satisfactory results and surpasses the performance of individual methods.

Page 2 of 2 | Total Record : 17

 1   2 

Filter by Year

2024 2024


Reset
Filter By Issues
All Issue Vol 13, No 4: December 2025 (ACCEPTED PAPERS) Vol 13, No 3: September 2025 Vol 13, No 2: June 2025 Vol 13, No 1: March 2025 Vol 12, No 4: December 2024 Vol 12, No 3: September 2024 Vol 12, No 2: June 2024 Vol 12, No 1: March 2024 Vol 11, No 4: December 2023 Vol 11, No 3: September 2023 Vol 11, No 2: June 2023 Vol 11, No 1: March 2023 Vol 10, No 4: December 2022 Vol 10, No 3: September 2022 Vol 10, No 2: June 2022 Vol 10, No 1: March 2022 Vol 9, No 4: December 2021 Vol 9, No 3: September 2021 Vol 9, No 2: June 2021 Vol 9, No 1: March 2021 Vol 8, No 4: December 2020 Vol 8, No 3: September 2020 Vol 8, No 2: June 2020 Vol 8, No 1: March 2020 Vol 7, No 4: December 2019 Vol 7, No 3: September 2019 Vol 7, No 2: June 2019 Vol 7, No 1: March 2019 Vol 6, No 4: December 2018 Vol 6, No 3: September 2018 Vol 6, No 3: September 2018 Vol 6, No 2: June 2018 Vol 6, No 1: March 2018 Vol 5, No 4: December 2017 Vol 5, No 3: September 2017 Vol 5, No 2: June 2017 Vol 5, No 1: March 2017 Vol 4, No 4: December 2016 Vol 4, No 3: September 2016 Vol 4, No 2: June 2016 Vol 4, No 1: March 2016 Vol 3, No 4: December 2015 Vol 3, No 3: September 2015 Vol 3, No 2: June 2015 Vol 3, No 1: March 2015 Vol 2, No 4: December 2014 Vol 2, No 3: September 2014 Vol 2, No 3: September 2014 Vol 2, No 2: June 2014 Vol 2, No 1: March 2014 Vol 1, No 4: December 2013 Vol 1, No 3: September 2013 Vol 1, No 2: June 2013 Vol 1, No 1: March 2013 More Issue