TELKOMNIKA (Telecommunication Computing Electronics and Control)
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Modeling and optimization of artificial magnetic conductor on the performance of on-chip-antenna for 28 GHz devices
Full text linkThe growing popularity of chip-based devices has spurred interest in developing on-chip antennas (OCAs). However, low gain and poor radiation characteristics have been significant challenges. Integrating an artificial magnetic conductor (AMC) into the oxide layer is an alternative. This article presents a new AMC model that improves the performance of 28 GHz OCA using dual-rectangular-patch (DRP) as unit cells. The DRP parameters, patch width (Pw), patch gap (Pg), and substrate height (hs) were used to control the AMC characteristic. Two numerical equations for gain (G) and efficiency (η) have been developed to evaluate the new model’s performance. The impact of parameters on the antenna’s gain and radiation efficiency was equally analyzed. A prototype antenna was fabricated and tested to validate the model. It demonstrated a peak gain of 3.69 dB and radiation efficiency of 67.18%, with an achieved impedance bandwidth of 1.27 GHz, making it well-suited for 28 GHz device applications. Furthermore, the equations formulated provide the research community with a straightforward method to calculate the gain and efficiency of a 28 GHz antenna. This method is not limited to on-chip antennas but can also be applied to off-chip antennas if DRP-AMC is implemented
Novel intelligent TOPSIS variant to rank regions for disaster preparedness
Full text linkAn important facet of disaster mitigation is discovering regions based on their lack of preparedness for combating disaster. Accordingly, organizations can lay down appropriate risk management strategies and guidelines to minimize loss due to disaster. “Techniquefor order of preference by similarity to ideal solution(TOPSIS)” is a popular multi-criteria decision-making (MCDM) method that is deployed for ranking alternatives based on multiple pre-specified criteria. However, the method’s efficiency in ranking region as per multiple criteria for disaster management is far from the ground truth. The authors propose a novel intelligent method HCF-TOPSIS, an extension of traditional TOPSIS, to deliver an efficient ranking mechanism for regional safety assessment of disaster affected regions. HCF-TOPSIS capitalizes on entropy (H), closeness (C),and farness (F) metrics to obtain efficient ranking scores of the disaster affected regions. Extensive experimentation validates the claim and proves the superiority of HCF-TOPSIS over existing TOPSIS variants. The proposed research presents many benefits, especially to governments and stakeholders, intending to take appropriate actions to contain disasters
Effectiveness of Bluetooth communication of digital stethoscope using quality of service method
Full text linkThis study aims to design and evaluate a wireless electronic stethoscope that can transmit heart sound using Bluetooth HC-05 and Bluetooth 5.0 transmitter. This novel design contributes to the remote diagnosis and monitoring of heart conditions, especially for patients with infectious diseases. The heart sound signals are captured using a mic condenser mic, amplified, filtered, and converted to digital data by a microcontroller. The data are then transmitted by Bluetooth HC-05 to a module and by Bluetooth 5.0 transmitter to a headset. The quality-of-service parameters such as throughput, delay, and packet loss ratio (PLR) of the data transmission at different distances are measured. The results show that the wireless electronic stethoscope can transmit heart sound data with a small PLR of 0.10% and a throughput of 1002.5 bps. The study concludes that the wireless electronic stethoscope is an effective and useful device for examining heart conditions remotely, without compromising the functionality of the devic
Deep learning based phishing website detection
Full text linkPhishing attacks use fraudulent websites that trick people into disclosing sensitive information. More effective and precise methods are required to identify phishing websites so that people and organisations can be protected from the damaging effects of these online threats. The aim of this work is to develop a model that can identify phishing uniform resource locator (URLs) more accurately than current approaches while requiring less training time, testing time, and storage space. This research work proposes a novel method for identifying phishing websites using a long short-term memory (LSTM) gated recurrent unit (GRU) algorithm to detect phishing URLs. The accuracy of the suggested method is 98.89%, which is significantly better than the findings of earlier studies. The model also showed a need for shorter training and testing time, and a reduced amount of storage space
Enhanced fuzzy logic control for overcoming intrinsic resistance in inverted pendulum systems
Full text linkThe paper delves into an in-depth analysis of the intrinsic resistance of the inverted pendulum system which causes the modeling of the system to differ from the actual system. Our primary objective revolves around the implementation and subsequent optimization of fuzzy logic controllers (FLC), drawing inspiration from human perceptual assessments. The processing comprises comprehensive mathematical system modeling, intrinsic resistance examination, and improved fuzzy logic control with detailed membership function and rule design. In addition, we conduct a comparative analysis with the widely recognized linear quadratic regulator (LQR) algorithm, which is considered the conventional control algorithm. The result demonstrates that the improved FLC outperforms the conventional LQR algorithm overshoot mitigation, thereby underscoring its superior efficacy and optimality
The impact of software metrics in NASA metric data program dataset modules for software defect prediction
Full text linkThis paper discusses software metrics and their impact on software defect prediction values in the NASA metric data program (MDP) dataset. The NASA MDP dataset consists of four categories of software metrics: halstead, McCabe, LoC, and misc. However, there is no study showing which metrics participate in increasing the area under the curve (AUC) value of the NASA MDP dataset. This study utilizes 12 modules from the NASA MDP dataset, where these 12 modules are being tested into 14 relationships of software metrics derived from the four existing metric categories. Subsequently, classification is performed using the k-nearest neighbor (kNN) method. The research concludes that software metrics have a significant impact on the AUC value, with the LoC+McCabe+misc metrics relationship influencing the improvement of the AUC value. However, the metrics relationship that has the most impact on achieving less optimal AUC values is McCabe. Halstead metric also plays a role in decreasing the performance of other metrics
Automated classification of diseased cauliflower: a feature-driven machine learning approach
Full text linkCauliflower is a popular winter crop in Bangladesh. However, cauliflower plants are vulnerable to several diseases that can reduce the cauliflowers’ productivity and degrade their quality. The manual monitoring of these diseases takes a lot of effort and time. Therefore, automatic classification of the diseased cauliflower through computer vision techniques is essential. This study has retrieved ten different statistical and gray-level co-occurrence matrix (GLCM)-based features from the cauliflower image dataset by implementing a variety of image processing techniques. Afterwards, the SelectKBest method with the analysis of variance f-value (ANOVA F-value) has been used to identify the most important attributes for classification of the diseased cauliflower. Based on the ANOVA F-value, the top N (5≤N ≤9) most dominant attributes is used to train and test five machine learning (ML) models for classification of diseased cauliflower. Finally, different performance metrics have been used for evaluating the effectiveness of the employed ML models. The bagging classifier achieved the highest accuracy of 82.35%. Moreover, this model has outperformed other ML classifiers in terms of other performance metrics also
Design of defective ground plane modified microstrip patch antenna for ultra-wideband applications
Full text linkThis study proposes a modified ultra-wideband (UWB) patch antenna with defective ground plane layout on FR-4 substrate material has dielectric constant ℇr equals to 4.3. An altered feed line has been employed to considerably enhance the antennas performance. Starting from 4 GHz to 13 GHz, upper, and lower frequency ranges can produce UWB antenna capabilities. The proposed antenna has a good bandwidth, making it practical to use in a variety of applications. Over the operational band, the reflection coefficient is decreased to less than -10 dB. The finite integral approach of fit is used to construct and analyze the antenna utilizing the computer simulation technology CST package simulator. In this study, an UWB antenna is demonstrated that may offer notches in the lower UWB band (3.1- 4 GHz). The performance of the antenna has been enhanced by using the flawed ground structure. The return losses and radiation characterstic confirm that the intended notched frequency has been suppressed. The proposed antenna was designed and simulated using computer simulation technology (CST 2020)
Telemedicine-based baby incubator system with DWT method to detect respiratory rate from electrocardiogram signals
Full text linkThis pioneering research introduces a telemedicine-based baby incubator designed to accurately monitor premature babies’ electrical heart signals and respiratory rates. The research aims to monitor premature babies, especially those related to heart and respiratory problems. This research combines telemedicine technology with the discrete wavelet transform (DWT) method to obtain respiratory rate values from electrocardiogram (ECG) signal leads directly. This research contribution can be used for simultaneous and non-invasive monitoring of heart electrical signals and respiratory rate. By leveraging existing telemedicine infrastructure, this incubator enables real-time monitoring. Using a data-driven approach with premature babies as subjects, respiratory signals are captured using sensitive sensors and analyzed via the DWT method. The results show that the accuracy of this telemedicine-based incubator is superior in monitoring respiratory rate compared to conventional methods with a P-value>0.05. This study confirms the effectiveness of DWT-based telemedicine incubators in monitoring the breathing of premature babies, thereby offering superior performance compared to traditional methods. The implications resulting from this research as a whole, this telemedicine-based baby incubator marks a significant advancement in the care of premature babies, ensuring precise and real-time breathing monitoring
Flood vulnerability index to aid decision making
Full text linkFloods damage ecosystem of the affected area resulting in destruction, loss of asset and life. The paper proposes a novel k-FVI, (k stands for Kerala and FVI for flood vulnerability index) to aid the decision makers reduce flood vulnerability of 14 districts of Kerala. Instead of usual classification of flood vulnerability indicators under exposure (), sensitivity (), and adaptive capacity (ℂ), k-FVI proposes that, indicators reflecting and preparedness (ℙ)govern pre-flood vulnerability, whereas those of and rehabilitation (ℝ) affects post-flood vulnerability. The division of ℂ indicator into ℙ and ℝ indicators and clubbing them into pre-flood and post-flood vulnerability respectively results into reduced errors. The importance of high dimensional flood indicators is realized by measuring the entropy of affected areas. Use of technique for order preference by similarity to ideal solution (TOPSIS) and entropy-based weights to score flood affected area results in formulating robust k-FVI. The paper also compares k-FVI with existing FVIs in literature. It uses data of 2018 Kerala floods and assesses the flood vulnerability of its 14 districts. The results prove that k-FVI is an effective flood vulnerability score estimator. Variant of k-FVI can be used to obtain vulnerability for any other flood prone areas