TELKOMNIKA (Telecommunication Computing Electronics and Control)
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A new resonant-based sensor for non-invasive measurement of blood glucose levels
Full text linkThis paper presents a rapidly developed non-invasive microstrip sensor for measuring blood glucose levels (BGLs). The sensor features a microstrip closed-loop square resonator integrated with an interdigital capacitor (IDC), creating a sensitive area for glucose detection when a patient’s finger is placed on it. Using odd and even mode analytical methods and transmission line theory, we analyzed the sensor’s performance. Results indicate that the second even mode demonstrates significant changes across a standard glucose concentration range. The sensor was designed and simulated in ANSYS high frequency structure simulator (HFSS), showing a resonance frequency shift of up to 24.9 MHz at 1.94 GHz and a sensitivity of 110 kHz per mg/dL over a detection range of 0 to 216 mg/dL. Additionally, the frequency shift exhibits a high linear correlation (0.9485). In summary, the proposed sensor shows significant promise for achieving precise measurements of BGLs
Reconfigurable ultra-wideband hexagonal antenna with two notched-band features for wireless applications
Full text linkOwing to the demand for frequency agility, a switchable ultra-wideband (UWB) hexagonal antenna was developed in this study. The proposed antenna features two notch filters introduced by two U-shaped slots on the patch to reduce interference from other wireless networks by rejecting the unique frequency bands. In addition, the proposed antenna comprises a hexagonal radiator attached to a feeding 50 Ω standard microstrip line. To fabricate the antenna prototype, a substrate (Rogers RT/Duroid 5880) with loss tangent and relative permittivity values of 0.0009, and 2.2, respectively, was used. Frequency and pattern reconfigurability were achieved by changing the electrical equivalent circuit of two positive-intrinsic-negative (PIN) diodes sandwiched within two U-shaped slots. The evaluation confirmed that the antenna operated within the D1&D2-ON configuration across the entire UWB range while, effectively filtering the wireless body area network (WBAN) (6.10–6.56 GHz) and radar application (9.16–10.79 GHz) bands when both diodes were OFF. The radiation efficiency and gain reached values of 92.9 % and 7.5 dB, respectively. The proposed design offers a robust performance with enhanced interference rejection. This makes it suitable for modern cognitive radio systems
Energy analysis and comparative study of n-wheel graphs in hierarchical wireless sensor network architectures
Full text linkThe energy analysis of the newly introduced n-wheel graph, employs diverse matrix representations such as the adjacency matrix, Laplacian matrix, and maximum degree matrix. This novel graph model resembles a hierarchical wireless sensor network (WSN), with a central hub serving as the communication center. The graph is organized into cycles, reflecting tiers of devices or sensors, with the hub managing wireless communication across these tiers. Through comparative analysis of energy variations, particularly focusing on ordinary energy, Laplacian energy, and maximum degree energy, offers a deeper understanding on the potential benefits of the n-wheel graph model, guiding future research and practical applications in the design of advanced hierarchical network structures
Enhancing realism in hand-drawn human sketches through conditional generative adversarial network
Full text linkThis research focuses on enhancing the realism of hand drawn human sketches through the use of conditional generative adversarial networks (cGAN). Addressing the challenge of translating rudimentary sketches into highfidelity images, by leveraging the capability of deep learning algorithms such as cGANs. This is particularly significant for applications in law enforcement, where accurate facial reconstruction from eyewitness sketches is crucial. Our research utilizes the Chinese University of Hang Kong Face Sketches (CUFS) dataset, a paired dataset of hand drawn human faces sketches and their corresponding realistic images to train the cGAN model. Generator network produces realistic images based on input sketches, where as discriminator network evaluates authenticity of these generated images compared to the real ones. The study involves careful preprocessing of the dataset, including normalization and augmentation, to ensure optimal training conditions. The model performance assessed through both quantitative metrics, such as frechet inception distance (FID), and qualitative evaluations, including visual inspection of generated images. The potential applications of this research extend to various fields, such as agencies of law enforcement for finding suspects and locating missing persons. Future work exploring advanced techniques for further realism, and evaluating the model’s performance across diverse datasets
Experimental validation of positioning and tracking system using ultra-wideband and low-cost microcontroller units
Full text linkIndoor positioning systems (IPS) have become increasingly critical in various applications, from asset tracking to smart environments. While global positioning system (GPS) offers precise outdoor localization, its signal is unavailable indoors. Ultra-wideband (UWB) technology emerges as a promising alternative due to its high accuracy, robustness against multipath interference, and ability to operate in dense environments. Aiming to develop an affordable and efficient system, we present a UWB-based IPS using the DW1000 UWB chip, evaluated with two different low-cost microcontroller units (MCUs): the ESP8266 system-on-chip (SoC) and the Arduino Uno R3. The findings suggest that the ESP8266 SoC is a superior choice for building an affordable and efficient UWB IPS, making it a compelling option for widespread adoption in budget-sensitive applications
Enhanced microwave absorption in partition walls using rice husk biomass composites
Full text linkCurrently, electromagnetic pollution (EM) has shown adverse complications and resulted in very negative effects on human health. Therefore, developing efficient microwave absorbers to reduce EM is important. This study aims to produce an anti-microwave partition wall using a biomass composite, which is a combination of rice husk and palm oil fuel ash (POFA). The goal of this project is to produce a partition wall that can act as a microwave absorber and create a healthy and safe environment. The anti-microwave radiation partition wall is designed using rice husk, POFA, cement, water, aluminium, and gypsum board. The composition ratios tested were rice husk content (5% and 20%) and POFA content (25%, 35%, and 45%). This mixture is also supplemented with aluminum (0.5% and 3.0%). The Naval Research Laboratory (NRL) Arch free space method determines the reflectivity performance of anti-microwave partition walls in the frequency range from 1 GHz to 12 GHz. Observation shows that the anti-microwave radiation partition wall for prototype BR5 containing 5% rice husk, 25% POFA, and 0.5% aluminium has the best reflectivity performance
Design and simulation of rectangular patch antenna arrays with high bandwidth for 2.4 GHz ISM band applications
Full text linkOngoing advancements in microstrip patch antenna (MPA) development research is driven by its compact size, cost-effectiveness and ease of fabrication. This paper presents a flexible design of patch antenna array (PAA) to address bandwidth (BW) limitations within the 2.4 GHz industrial, scientific, and medical (ISM) band, where narrow BW is a common challenge. To explore the effectiveness of different array configurations, we designed and evaluated 1×2, 2×2, and 2×4 element rectangular PAA, employing a quarter-wave transformer (QWT) method and parallel scheme for connecting patches. By utilizing Ansys high frequency structure simulator (HFSS) as the modeling environment, we conducted extensive simulations to refine the antenna parameters and achieve the most optimal MPA prototype. Our investigation demonstrates sufficiently good results, including BWs of 290 MHz and 210 MHz for 2×2 and 2×4 PAAs respectively, which account for 8.75% and 12% of the total value. The parameter return loss (RL) (S_11) reached -51dB for single-element patch antenna (SPA) and -37.5 dB for 8-element PAA, that shows an ideal impedance matching. In addition, the designed 2×4 PAA exhibited impressive performance metrics, accounting for 9.17 dB in gain, 13 dBi in directivity, and voltage standing wave ratio (VSWR) maintained below 0.5, ensuring excellent signal transmission and reception
Integrating artificial intelligence into accounting systems: a qualitative study on user experiences and challenges
Full text linkThis research explores the integration of artificial intelligence (AI) in accounting systems, focusing on user experiences and challenges faced by accountants and financial professionals. Using qualitative methods, in-depth interviews with diverse accounting professionals reveal key themes: optimism mixed with skepticism about AI’s potential, concerns over algorithm transparency, and trust issues due to the “black box” nature of AI systems. Participants highlight inadequate training programs, which hinder effective AI use and fuel resistance to adoption. The study also discusses the impact of AI on job roles, emphasizing a shift towards strategic thinking and advisory functions while routine tasks are automated. Implementation challenges include system compatibility, data integration issues, and significant resource investments, compounded by organizational resistance and lack of executive support. The findings stress the need for transparent AI algorithms, comprehensive training programs, and managed job role transitions to maximize AI benefits. This research provides insights into real-world user experiences, offering a roadmap for organizations to support effective AI integration in accounting, leading to improved performance, job satisfaction, and acceptance of AI technologies
Advanced signal transformation techniques to improve spectral efficiency in visible light communication systems
Full text linkVisible light communication (VLC) offers high-speed wireless communication using the visible light spectrum. Achieving high spectral efficiency while maintaining a low bit error rate (BER) remains a challenge. This paper explores the use of quadrature amplitude modulation (QAM) combined with orthogonal frequency division multiplexing (OFDM) to address these challenges. Matrix laboratory (MATLAB) simulations show that QAM-OFDM achieves a BER of 0.001 at comparable signal-to-noise ratios (SNR), outperforming traditional hermitian symmetry (HS), complex signal mapping (CSM), and quad-light emitting diode (LED) complex modulation (QCM) techniques. Unlike CSM, and QCM, which increase complexity, and BER, QAM-OFDM efficiently utilizes available bandwidth, reducing errors, and enhancing spectral efficiency. The study concludes, that QAM-OFDM happens to be the optimal solution for the future VLC systems, offering better performance within both efficiency, and reliability
A comparative analysis of transfer learning models on suicide and non-suicide textual data
Full text linkThe rise of social media has allowed individuals to express themselves freely, increasing the visibility of mental health concerns, including suicidal tendencies. This issue is particularly significant, as suicide is one of the leading causes of death globally. The objective of this study is to develop a model capable of accurately detecting suicide-related textual data using advanced natural language processing techniques. To achieve this, we applied transfer learning models, including bidirectional encoder representations from transformers (BERT), robustly optimized bidirectional encoder representations from transformers (RoBERT), a lite BERT (ALBERT), and decoding-enhanced BERT with disentangled attention (DeBERTa). the dataset used in this research includes 232,074 posts from Reddit, categorized into suicide and non-suicide labels. Preprocessing steps such as removing HTML tags, special characters, and punctuation were applied, followed by stopword removal and lemmatization. The models were trained and evaluated using accuracy, precision, recall, and F1-score metrics. Among the models tested, DeBERTa demonstrated superior performance, achieving an accuracy of 98.70% and an F1-score of 98.70%. These findings suggest that transfer learning models, particularly DeBERTa, are effective in identifying suicidal ideation in textual data