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INDONESIA
Indonesian Journal of Electrical Engineering and Computer Science
Published by Institute of Advanced Engineering and Science
ISSN : 25024752     EISSN : 25024760     DOI : -
Core Subject :
Arjuna Subject : -
Articles 66 Documents
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Search results for , issue "Vol 34, No 2: May 2024" : 66 Documents clear
An Intrusion Detection System against RPL-based Routing Attacks for IoT Networks Manjula Hebbaka Shivanajappa; Roopa Maidanahalli Seetharamaiah; Bharath Viswaraju Sai; Arunalatha Jakkanahally Siddegowda; Venugopal Kuppanna Rajuk
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp1324-1335

Abstract

The significant improvement in the Internet, Internet of Things (IoT), communication, and cloud computing have created considerable challenges in providing security for data and devices.  In IoT networks, “Routing Protocol for Low power and Lossy networks”- (RPL) is a communication protocol that enables devices to exchange information and communicate with limited resources like low processing capabilities, less memory and energy. Through the Internet, unauthorised users can access RPL-based IoT networks, making these networks susceptible to routing attacks. Therefore, it is crucial to design Intrusion Detection System-(IDS) to address attacks from IoT communication devices. In this paper, we have proposed GCNConv, a Graph Neural Network (GNN) method that allows capturing the edge and node features of a graph to identify routing attacks. The proposed   system   has experimented on the RADAR dataset and experimental findings proved that, our approach performs well compared to state-of-the-art method with reference to precision, F1-score, accuracy and recall.
Mobilenet, inception ResNet and GoogleNet for epilepsy detection using spectrogram images Fatima Edderbali; Mohammed Harmouchi; Elmaati Essoukaki
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp870-877

Abstract

Epilepsy is considered the most common cerebral disorder, around 1% of the worldwide population suffer from it. Recently, detection of epilepsy has attracted more and more attention. It has become a hastily increasing problem that can worsen their conditions which necessitate a specific and crucial attention where the symptoms can be an impaired awareness or motor symptoms. Besides that, the difficult process of manual inspection of electroencephalography electroencephalogram (EEG). This paper proposes using transfer learning models to detect both normal and epileptic brain activity and auto-classify signals from the brain. The models considered for this study are GoogleNet, MobileNet, and inception residual neural network inception ResNet. These models were associated with seven different classifiers such as discriminant. These classifiers were tested, analyzed and compared with each other. The efficiency of models is comparatively evaluated through result using multiple metrics. We therefore attained an accuracy of 96.53%, a precision of 97.18%, a false positive rate of 2.78% and an F1-score of 96.50%. Finally, comparison of the suggested approach with existing research shows that the performance of epilepsy classification has been markedly enhanced.
FPGA-base object tracking: integrating deep learning and sensor fusion with Kalman filter Abdoul Moumouni Harouna Maloum; Nicasio Maguu Muchuka; Cosmas Raymond Mutugi Kiruki
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp888-899

Abstract

This research presents an integrated approach for object detection and tracking in autonomous perception systems, combining deep learning techniques for object detection with sensor fusion and field programmable gate array (FPGA-based) hardware implementation of the Kalman filter. This approach is suitable for applications like autonomous vehicles, robotics, and augmented reality. The study explores the seamless integration of pre-trained deep learning models, sensor data from a depth camera, real-sense D435, and FPGA-based Kalman filtering to achieve robust and accurate 3D position and 2D size estimation of tracked objects while maintaining low latency. The object detection and feature extraction are implemented on a central processing unit (CPU), and the Kalman filter sensor fusion with universal asynchronous receiver transmitter (UART) communication is implemented on a Basys 3 FPGA board that performs 8 times faster compared to the software approach. The experimental result provides the hardware resource utilization of about 29% of look-up tables, 6% of lookup table RAMs (LUTRAM), 15% of Flip-flops, 32% of Block-RAM, 38% of DSP blocks operating at 100 MHz, and 230400 baud rates for the UART. The whole FPGA design executes at 2.1 milliseconds, the Kalman filter executes at 240 microseconds, and the UART at 1.86 milliseconds.
Modeling and adaptive neuro-fuzzy inference system control of quarter electric vehicle Rachida Baz; Khalid El Majdoub; Fouad Giri; Ossama Ammari
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp745-755

Abstract

Electric vehicles (EVs) have gained importance in recent years, prompting the development of several control systems to improve their efficiency and performance. In this work, a quarter electric vehicle (QEV) was controlled using a conventional proportional integral derivative (PID) and fuzzy controller to examine and compare with the response of the adaptive neuro-fuzzy inference system (ANFIS) controller. The response of the ANFIS controller was evaluated using MATLAB/Simulink according to different parameters and compared with those of other controllers. In addition, the simulation was based on different driving conditions such as the acceleration and deceleration modes and the type of road: wet and dry. The simulations were carried out on a longitudinal electric vehicle model based on a brushless DC motor, including the Pacejka tire model. The results showed that the ANFIS controller outperformed the PID and fuzzy logic controllers, providing superior dynamic responsiveness and stability when the ANFIS controller smoothly followed the input speed and the longitudinal slip value reached 3%.
Refining tomato disease recognition: hyperparameter tuning on ResNet-101 architecture for precise leaf-based classification Tegar Arifin Prasetyo; Tiurma Lumban Gaol; Nico Felix Sipahutar; Tessalonika Siahaan; Trito Exaudi Manik; Rudy Chandra
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp1204-1213

Abstract

Tomatoes plants are widely recognized as versatile vegetables globally. This study aims to develop a high-precision web interface for classifying various leaf diseases in tomatoes. Utilizing a convolutional neural network (CNN) algorithm using the residual network-101 (ResNet-101) architecture, this tool aids farmers in accurately identifying leaf diseases in tomatoes, thereby reducing the risk of crop failure. The dataset comprises 6,800 images, categorized into four classes: early blight, spider mites two spotted, tomato yellow leaf curl virus, and healthy tomatoes, each containing 1,700 images. Hyperparameter tuning was conducted as part of an experiment aimed at enhancing the performance of the model. Experiments involved varying epoch values (10, 25, 30, 50, 60, 75, 100, and 110), a fixed batch size of 4, different learning rates (0.1, 0.01, 0.001, 0.0001), and num workers (4, 8, 16). The results demonstrated an accuracy of 99% with 100 epochs, a batch size of 4, a learning rate of 0.001, and 16 num workers. Consequently, this research contributes to a deeper understanding of disease management in tomato plants, ensuring optimal quality and quantity of the harvest.
Support vector regression-based state of charge estimation for batteries: cloud vs non-cloud Mohamed Ben Youssef; Imen Jarraya; Mohamed Ali Zdiri; Fatma Ben Salem
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp697-710

Abstract

Embracing the potential of cloud technology in the field of electric vehicle advancements, this paper explores the application of support vector regression (SVR) for accurate state of charge (SOC) estimation of lithium-ion batteries in various computational landscapes. This study aims to scrutinize and compare the performance of SOC estimation, with a specific focus on precision, computational efficiency, and execution speed. The investigation is conducted across diverse environments, including a traditional non-cloud setup and two cloud-based platforms-a standard cloud environment employing Amazon web services (AWS) EC2 servers and an enhanced configuration utilizing the MATLAB production server. The investigation not only emphasizes the effectiveness of cloud integration but also provides valuable insights into the strengths and weaknesses of the proposed methodology. The experimental results contribute to a nuanced understanding of the methodology’s performance, shedding light on its potential implications for advancing electric vehicle technologies. This study thus extends its significance beyond technical considerations, providing a broader perspective on its relevance to global electrification initiatives.
The synergistic effect of QR decomposition with t-SNE Mohsin Ali; Jitendra Choudhary
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp1159-1169

Abstract

The study utilized non-parametric tests, specifically, the Mann-Whitney U test, to evaluate the performance of a proposed model called QRPCA-t-SNE, along with two other models, MDS and UMAP. The study compared these three models with two datasets on performance metrics such as model accuracy, training accuracy, testing accuracy, mean square error, AUC scores, precision, recall, and F1 scores. Once the model's performance was conducted, the Anderson-Darling test was to check for data normality before applying the hypothesis for model proof. The analysis revealed that Model 1 (QRPCA-t-SNE) significantly outperformed Model 2 (UMAP) and Model 3 (MDS) in terms of accuracy, with p-values of 0.0027 and 0.0003, respectively. This finding suggests that Model 1 (QRPCA-t-SNE) is suitable for high-accuracy and reliability applications, providing valuable insights into predictive analytics with a 95% confidence interval (confidence level α= 0.05).
A standard ranking algorithm for robust iris template protection Mohammed Ali Hameed Yassir; Rudzidatul Akmam Dziyauddin; Norshaliza Kamaruddin; Norulhusna Ahmad
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp1214-1225

Abstract

In iris biometric recognition systems, protecting the storage and transmission of iris templates is crucial, and template protection techniques are pivotal for ensuring their security. A prevalent approach involves using indexing methods as an effective algorithm for iris template protection, leveraging the index or rank of the extracted iris code to generate a secure iris template. Meantime, many privacy threats to biometric data have emerged, necessitating heightened protection measures. Specifically, protecting the privacy of iris data is imperative within the context of iris template protection during recognition processes. As stipulated by the international standard ISO/IEC 30136, effective iris template protection must concurrently meet the criteria of irreversibility, revocability, and unlinkability. Nevertheless, existing indexing methods on iris template protection faced the formidable challenge of simultaneously fulfilling these three privacy requirements while maintaining the efficacy of iris recognition. This paper introduces a standard ranking (standardR) algorithm, named standardR, designed to enhance the security of iris templates by transforming each iris template into an irreversible representation. The experimental results on the benchmarked Casia-Iris-interval dataset, along with two additional iris datasets MMU1 and UBRIS 1, demonstrate the efficacy of the proposed algorithm. The proposed standardR algorithm achieves an equal error rate (EER) of 0.1695% and an area under the curve of 0.93011% with the Casia-Iris-Interval dataset. Furthermore, the algorithm maintains efficient recognition with a reduced iris code length of 1280 bits, a time complexity of O(n log n), and satisfies the biometric template protection (BTP) requirements in irreversibility, unlinkability, and renewability.
An internet of things-based pump and aerator control system Mawardi Mawardi; Panangian Mahadi Sihombing; Nabila Yudisha
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp848-860

Abstract

Small-scale shrimp farmers in Hamparan Perak District, Deli Serdang Regency, Indonesia, conduct direct water quality supervision and manually use aerators and water pumps. Thus, it is inefficient in meeting the water quality required for shrimp farming and using production costs. This study aims to test the performance of an internet of things (IoT)-based prototype in supervising and controlling the aerator and pump in a shrimp pond. This prototype comprises an ESP32, three sensors: the DS18B20 sensor, MLX90614 sensor, and JSN-SR04T sensor, and two relays to control the aerator and pump automatically. Prototype testing is done directly on shrimp ponds by placing the prototype in an electrical panel connected to a power circuit. Based on the study's results, it is known that the prototype can measure water temperature. The water level and temperature of the aerator motor are pretty accurate. In addition, the prototype can also control the aerator and water pump well and send notifications to users automatically via smartphones.
Enabling low-latency IoT communication for resource-constrained devices with the led cipher and decipher protocol Mahendra Shridhar Naik; Desai Karanam Sreekantha; Kanduri VSSSS Sairam
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp1170-1180

Abstract

Block cipher algorithms are crucial for securing applications on resource-constrained devices. This paper introduces the modified light encryption device (MLED) cipher-decipher architecture, specifically designed to accommodate both 64-bit and 128-bit key sizes while maintaining a consistent 64-bit block and data size. MLED comprises 8-step and 12-step processes for MLED-64 and MLED-128 modules, respectively. Each stage involves a four-round operation followed by an add-round key operation. The add constant module (ACM) and mixed column modules (MCMs) within the round operation have been optimized for improved latency and throughput. Performance analysis reveals that MLED-64/128 requires less than 1% of the available slices and operates at 125 MHz on the Artix-7 FPGA. It achieves delays of 7.5 and 12.5 clock cycles for MLED-64 and MLED-128, respectively, translating to throughputs of 1366.5 Mbps and 819.89 Mbps. Additionally, MLED-64/128 exhibits hardware efficiencies of 2.373 and 0.986 Mbps/slice, respectively. Comparative evaluations with existing LED and other block ciphers (BCs) demonstrate that MLED-64/128 achieves significant improvements in latency, throughput, and efficiency, making it a compelling choice for securing resource-constrained IoT applications.

Page 6 of 7 | Total Record : 66

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