Indonesian Journal of Electrical Engineering and Computer Science
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Effective medium ratio obeying metamaterial absorber for 5G sub-7 GHz and sub-8 GHz applications
Get PDFMetamaterials possess the capability to enhance fifth-generation (5G) communication technology. This article proposes an innovative construction of a miniature metamaterial absorber (MMA) with a dramatically improved effective medium ratio (EMR) characterized by utilizing a multi-square split-ring resonator (MSSRR) MMA unit cell specifically designed for operation in the 5G sub-7 GHz and Sub-8 GHz frequency bands. The unit cell of the MMA is designed using a commercially available FR-4 material with εr=4.3, which is cost-effective. The proposed MMA achieves a remarkably high EMR of 9.83, indicating superior compactness and design efficiency. The MMA of interest operates with absorbance peaks of 70.632%, 96.936%, and 79.930% within the frequencies of 3.554 GHz, 4.940 GHz, and 8.335 GHz, respectively. Along with the absorption analysis, our examination also includes E-field, H-field, surface current, and power flow. The expected MMA has proven potential for application in some frequency bands related to 5G, released absorption signal, and specific absorption rate (SAR) assistance
Fuzzy medical expert system for prediction of prostate cancer
Get PDFWe developed the fuzzy medical expert system (F-MES) based on fuzzy inference system (FIS) Mamdani using a different approach to prostate cancer risk (PCR) prediction. The difference in our research is that we modify the membership function on the input variable according to medical standards. We used the same input variables as the previous study, namely age, prostate-specific antigen (PSA), prostate volume (PV), and percentage (%) free PSA (%FPSA). The data on the input variable is used as input into F-MES and displays the output in the form of a percentage (%) of PCR. If the PCR is >50%, then the patient is advised to undergo a biopsy test. We conducted an analysis with the doctor to create a simple domain and rule base of 24 rules. Our number of rules is lower than previous studies of 80 and 240, but our prediction results are better the F-MES evaluation used the same 56 patients, that the F-MES we developed had an accuracy of 857%. This score is better than previous studies of 75% and 76%. Our F-MES is simple but effective and can be used as a supporting tool in decision-making in medical diagnosis
Formalization of materialized view problem in ontology-based databases
Get PDFMaterialized views are essential for optimizing the performance of traditional databases and data warehouses by accelerating query responses. However, their substantial storage requirements and the impracticality of materializing all possible views raise the problem of selecting which views to persist, a fundamental physical design challenge. This article presents a rigorous formalization of this problem within the context of semantic databases. The methodology employed includes a comprehensive literature review aimed at identifying the variety of se-mantic database representations. This analysis revealed a significant diversity in data models and query languages used. Based on this analysis, a generic formalization framework is pro-posed. This framework enables the expression of various resolution approaches to the materialized view selection problem, taking into account the specificities of semantic databases. It offers broad applicability to any database management system, providing a common language to describe and compare view selection methods
Smart home advisory system based on IoT-enabled sensor network
Get PDFA consistent drive to make daily living more streamlined and less complex was critical throughout the industrial revolution. Although many applications are available, the general adoption of these applications is hindered by several factors (security concerns, expensive costs, and perceived usefulness). Hence, a cost-efficient smart home system can provide significant advantages without placing unnecessary financial or operational costs on consumers. This study proposed an inexpensive internet of things (IoT) enabled smart home advisory system for promoting healthier living conditions. The system contained numerous sensors for detecting surrounding brightness, temperature, humidity, and dust levels. Consequently, the system demonstrated effective real-time updates regarding the simulated temperature (22–32 °C), humidity (11–53 %RH), light (3–100 lx), and dust (0–278 mg/m3) environments. Blynk software was also embedded as an efficient user interface for the application. Overall, the lowcost IoT-enabled smart home advisory system offered concrete benefits by allowing users to improve their living surroundings while decreasing energy usage
A HBMO-based batch beacon adjustment for improving the Fast-RRT
Get PDFFast-RRT improves on the original rapidly-exploring random trees (RRT) by incorporating two main stages: improved-RRT and fast-optimal. The improved-RRT stage enhances the search process through fast-sampling and random steering, while the fast-optimal stage optimizes the path using fusion and path arrangement. However, path fusion can only be optimal when the newly found path is unique and different from previous paths. This uniqueness rarely occurs in cases with narrow corridors, so path fusion only provides suboptimal conditions. To address this, the study explores using honey bee mating optimization (HBMO) to optimize or replace the fusion stage. HBMO helps determine new beacon coordinates, which are nodes between the start and goal points along the path, through a batch beacon adjustment approach. The results show that integrating HBMO into FastRRT improves its optimality, with a 21.85% reduction in path cost and a 5.22% decrease in completion time across environments with varying difficulty levels. This hybrid algorithm outperforms previous methods in terms of both path optimality and convergence rate, demonstrating its effectiveness in enhancing Fast-RRT’s performance
Comparative analysis of incremental conductance MPPT for enhanced algorithm performance
Get PDFThe incremental conductance (INC) approach is limited in terms of its response speed, accuracy during steady state, and ability to handle oscillations. As a result, this algorithm is ineffective in situations with variations in solar radiation and temperature, particularly abrupt fluctuations. An enhanced variable step size INC approach is suggested to improve system efficiency and performance. The proposed algorithm is subjected to rigorous testing and analysis with other INC methods for better results. The research findings indicate that the proposed algorithm's tracking efficiency can be enhanced to 99.83% under varying radiation conditions and constant temperature. Additionally, the method utilizing the second model achieves a 99.83% tracking efficiency, while the method employing the first model achieves a tracking efficiency of 89.99%. In comparison, the conventional method achieves a tracking efficiency of 96.36%. Regarding radiation and temperature circumstances, the tracking efficiency value varies for different methods. Specifically, the tracking efficiency is 95.21% for the approach using the proposed algorithm’s, 92.94% for the method using the second model, 83,39% for the method using the first model, and 91.19% for the conventional way. Therefore, it can be inferred that the suggested Maximum power point tracking (MPPT) algorithm performs better than other algorithms under both test situations
Experimental research on text CAPTCHA of fine-grained security features
Get PDFCAPTCHA is a cybersecurity measure that distinguishes between humans and automated scripts. Researchers have employed various security features to thwart automated program identification by hackers. However, previous research on the attack resistance of CAPTCHAs has used roughly quantitative analysis instead of a fine-grain quantitative study. This study implemented comparative experiments based on CAPTCHA recognition algorithms to find the best-mixed security features. A multi-stage best parameter selection (MBPS) mechanism was proposed in this study. Experiment results indicated that mixed security features of “overlap + scale + rotate + bg (background)” were the best, with an average machine recognition accuracy of only 4.81%. The contrast experiment result illustrated that the anti-attack ability of mixed security features was better than adding adversarial noise, with machine recognition accuracy decreased by 2.2%. Moreover, by investigating the efficacy of security feature parameters, this study provides practical guidelines for designing robust CAPTCHAs. Furthermore, this study also presents valuable insights into the security of image generation technology
Innovating household efficiency: the internet of things intelligent drying rack system
Get PDFThe intelligent drying rack system (IIDRS) proposes an innovative approach to modernize clothes drying practices using internet of things (IoT) technology. Combining an Arduino Uno microcontroller, ESP8266 for data transmission, and an array of sensors including limit switches, light dependent resistors (LDRs), rain sensors, and temperature/humidity sensors, the IIDRS enables automated control of the drying rack and fan. Its remote accessibility via Blynk apps allows users to conveniently adjust settings and monitor drying progress. By autonomously adjusting drying cycles based on real-time environmental conditions, the IIDRS enhances efficiency and minimizes inconveniences such as wet clothes during rainfall. Moreover, it contributes to sustainable living by optimizing energy consumption through weather-based operation. With its intuitive interface and compatibility with modern lifestyles, the IIDRS represents a significant advancement in smart home solutions, showcasing the transformative potential of IoT technologies in everyday tasks
Leveraging 3D convolutional networks for effective video feature extraction in video summarization
Get PDFVideo feature extraction is pivotal in video processing, as it encompasses the extraction of pertinent information from video data. This process enables a more streamlined representation, analysis, and comprehension of video content. Given its advantages, feature extraction has become a crucial step in numerous video understanding tasks. This study investigates the generation of video representations utilizing three-dimensional (3D) convolutional neural networks (CNNs) for the task of video summarization. The feature vectors are extracted from the video sequences using pretrained two-dimensional (2D) networks such as GoogleNet and ResNet, along with 3D networks like 3D Convolutional Network (C3D) and Two-Stream Inflated 3D Convolutional Network (I3D). To assess the effectiveness of video representations, F1-scores are computed with the generated 2D and 3D video representations for chosen generic and query-focused video summarization techniques. The experimental results show that using feature vectors from 3D networks improves F1-scores, highlighting the effectiveness of 3D networks in video representation. It is demonstrated that 3D networks, unlike 2D ones, incorporate the time dimension to capture spatiotemporal features, providing better temporal processing and offering comprehensive video representation
Ensemble learning weighted average meta-classifier for palm diseases identification
Get PDFCrop diseases lead to significant losses for farmers and threaten the global food supply. The date palm, valued for its nutritional benefits and drought resistance in desert climates, is a vital export crop for many countries in the Middle East and North Africa, second only to hydrocarbons. However, various diseases pose a threat to this important plant. Therefore, early disease prediction using deep learning (DL) is essential to prevent the deterioration of date palm crops. The aim of this paper is to apply a robust ensemble method (EL) combining tree transfer learning (TL) models Resnet50, DenseNet201, and InceptionV3, and compares its performance with the CNN-SVM model and the tree TL models mentioned previously. The models were applied to a date palm dataset containing three classes: White scale, brown spot, and healthy leaf. The training and validation sets were applied to a public dataset, while the testing set was applied to a local dataset captured manually to check the model’s performance. As a result, we considered that the ensemble method gave very satisfactory results compared to other methods. Our hybrid model reached a testing accuracy of 98% while achieving an amazing training and validation accuracy of 99.94% and 98.14%, respectively