Articles

Access the latest knowledge in applied science, electrical engineering, computer science and information technology, education, and health.

Advanced Search
Filter Icon

Filters article

Years

FAQ Arrow
Single Year
Range Year
0
0

Source Title

FAQ Arrow

Authors

FAQ Arrow

31,456 Article Results

A new resonant-based sensor for non-invasive measurement of blood glucose levels

10.12928/telkomnika.v23i4.26280
Abolfazl; University of Birjand Bijari , Faeze; University of Birjand Foolad , Mohammadreza; University of Birjand Khorshidi
This 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.
Volume: 23
Issue: 4
Page: 1046-1057
Publish at: 2025-08-01

Advancing semiconductor integration: 3D ICs and Perylene-N as superior liner material for minimizing TSV clamour coupling

10.11591/ijict.v14i2.pp605-613
Pradyumna Kumar Dhal , Murkur Rajesh , Shaik Hussain Vali , Sadhu Radha Krishna , Malagonda Siva Kumar , Vempalle Rafi
The semiconductor industry faces substantial challenges with planar integration (2D ICs), prompting a significant shift towards vertical IC integration, known as three-dimensional IC (3D ICs). This deliberate slant not only amplifies bandwidth and boosts system action but also effectively reduces power consumption through scaling. 3D ICs intricately coordinate IC chips by vertically stacking them and establishing electrical connections using through silicon vias (TSVs). TSV clamour coupling emerges as a critical factor influencing system performance, particularly between signalcarrying TSVs (ETSV) and victim TSVs. This study showcases significant advancements in electrical integrity by effectively minimizing clamour coupling from TSVs to the silicon substrate. This is achieved through the application of CMOS-compatible dielectric materials as liner structures. Various proposed structures have been meticulously analyzed across an assortment of parameters, encompassing electrical signals and high frequencies. Moreover, the study rigorously investigates clamour coupling across different types of TSVs, including ETSV, thermal TSV (TTSV), and heat sources. Perylene-N emerges as a standout performer among the tested liner materials, demonstrating superior clamour coupling performance across all proposed models, even at higher frequencies such as THz. In this study a novel dielectric material Perylene-N compared with the conventional SiO2 (silicon dioxide). Notably, Perylene-N exhibited a remarkable 33 dB improvement in noise coupling performance at terahertz (THz) frequencies. The results were thoroughly verified and validated in the research work.
Volume: 14
Issue: 2
Page: 605-613
Publish at: 2025-08-01

Energy analysis and comparative study of n-wheel graphs in hierarchical wireless sensor network architectures

10.12928/telkomnika.v23i4.26499
Jerlinkasmir; Karunya Institute of Technology and Sciences Rubancharles , Naseema Valiyaveettil; KMEA Engineering college Abdul Lathief , Veninstine; Karunya Institute of Technology and Sciences Vivik Joseph
The 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.
Volume: 23
Issue: 4
Page: 932-942
Publish at: 2025-08-01

Perceived ease of use, usefulness, and task interdependence: impacts on employee performance in higher education

10.12928/telkomnika.v23i4.26699
Inayati; Universitas Sriwijaya Mandayuni , Kiagus Muhammad; Universitas Sriwijaya Sobri , Alfitri; Universitas Sriwijaya Alfitri , Andries; Universitas Sriwijaya Lionardo
This research aimed to investigate the relationship between perceived ease of use (PEU), perceived usefulness (PU), and task interdependence (TASKINT) on employees’ work performance. Technology acceptance model (TAM) was used as a theoretical perspective to explore technology adoption in the context of employees in higher education using electronic asset management (EAM). Moreover, a quantitative method was used to explain the causality of the relationship between the variables, and a total of 380 respondents were determined as the sample. The results showed that PEU and usefulness had a significant effect on TASKINT. Even though PEU and TASKINT had a significant effect on employees’ work performance, PU did not have a significant effect. In addition, the results showed TASKINT significantly mediated the relationship between perceived ease of use, usefulness, and employees’ work performance. These findings imply that enhancing the ease of use and fostering TASKINT can lead to improved employee performance when adopting new technologies. For higher education institutions (HEI), focusing on user-friendly systems and promoting collaborative tasks can maximize the benefits of technology implementation on work performance.
Volume: 23
Issue: 4
Page: 1108-1119
Publish at: 2025-08-01

An improved approximate parallel prefix adder for high performance computing applications: a comparative analysis

10.11591/ijict.v14i2.pp382-392
Vamsidhar Anagani , Kasi Geethanjali , Anusha Gorantla , Annamreddy Devi
Binary adders are fundamental in digital circuit designs, including digital signal processors and microprocessor data path units. Consequently, significant research has focused on improving adders’ power-delay efficiency. The carry tree adder (CTA) is alternatively referred to as the parallel prefix adder (PPA), is among the fastest adders, achieving superior performance in very large scale integrated (VLSI) implementations through efficient concurrent carry generation and propagation. This study introduces approximate PPAs (AxPPAs) by applying approximations in prefix operators (POs). Four types of AxPPAs approximate kogge-stone, approximate brent-kung, approximate ladner fischer, and approximate sparse kogge-stone-were designed and implemented on FPGA with bit widths up to 64-bit. Delay measurements from static timing analysis using Xilinx ISE design suite version 14.7 indicate that AxPPAs exhibit better latency performance than traditional PPAs. The AxPPA sparse kogge-stone, in particular, demonstrated superior area and speed performance, achieving a delay of 2.501ns for a 16-bit addition.
Volume: 14
Issue: 2
Page: 382-392
Publish at: 2025-08-01

Deep learning algorithms for breast cancer detection from ultrasound scans

10.11591/ijict.v14i2.pp427-437
Lawysen Lawysen , Gede Putra Kusuma
Breast cancer is a highly dangerous disease and the leading cause of cancer related deaths among women. Early detection of breast cancer is considered quite challenging but can offer significant benefits, as various treatment interventions can be initiated earlier. The focus of this research is to develop a model to detect breast cancer based on ultrasound results using deep learning algorithms. In the initial stages, several preprocessing processes, including image transformation and image augmentation were performed. Two types of models were developed: utilizing mask files and without using mask files. Two types of models were developed using four deep learning algorithms: residual network (ResNet)-50, VGG16, vision transformer (ViT), and data-efficient image transformer (DeiT). Various algorithms, such as optimization algorithms, loss functions, and hyperparameter tuning algorithms, were employed during the model training process. Accuracy used as the performance metric to measure the model’s effectiveness. The model developed with ResNet-50 became the best model, achieving an accuracy of 94% for the model using mask files. In comparison, the model developed with ResNet-50 and DeiT became the best model for the model without mask files, with an accuracy of 80%. Therefore, it can be concluded that using mask files is crucial for producing the best-performing model.
Volume: 14
Issue: 2
Page: 427-437
Publish at: 2025-08-01

Smart hybrid power management system in electric vehicle for efficient resource utilization with ANN

10.11591/ijict.v14i2.pp488-496
Vinoth Kumar Ponnusamy , Gunapriya Devarajan , Gomathi Easwaram , Geetha Murugesan , Rathinam Marimuthu Sekar , R. Delshi Howsalya Devi
The novel hybrid power system integrating energy storage, electric vehicle (EV) charging infrastructure and renewable energy sources improve sustainability and resilience. This work proposes a power management system controlled by artificial intelligence for EV charging infrastructure. The battery’s state of charge (SoC) is continuously monitored by artificial neural network (ANN) algorithm improves the health of the battery and efficient operation of the system. The buck boost DC-DC converter performs dynamic switching mechanism, which adjusts to changing solar conditions and energy demands, guarantees a steady power supply. Depending on the situation, the ANN algorithm used in the battery’s SoC control mechanism decides whether to priorities the EV charging or the inverter to supply the grid. The work is evaluated with the MATLAB simulation for different conditions and results are compared with different controllers such as PI, PID, and ANN. The experiment performed uses internet of things (IoT) for transferring the data from the EV motor, acts as an input for the controller to perform the novel hybrid power management operation with cloud technology. The experimental prototype evaluates the results connected to the photovoltaic (PV) system and battery management system (BMS) which lowers reliance on non-renewable resources, increases overall energy efficiency, and ensures a steady supply of power under a various condition.
Volume: 14
Issue: 2
Page: 488-496
Publish at: 2025-08-01

Performance analysis of LDPC codes in MIMO-OFDM for next generation wireless systems

10.11591/ijict.v14i2.pp636-644
P. Aruna Kumari , Srinu Pyla , U. N. V. P. Rajendranath , Nirujogi Venkata Maheswara Rao
Fifth Generation communication systems overcome the limitations of the fourth-generation systems and ensure improved data rates, lower latency, and higher connection density. 5G technology has the potential to unlock new internet of things (IoT) applications by utilizing the technologies such as multiple input multiple output orthogonal frequency division multiplexing (MIMO-OFDM), and Li-Fi. Low density parity check (LDPC) and polar codes are being preferred for data and control channels respectively in 5G systems as these coding techniques offer good error-detection and correction along with reduced latency. Morever, LDPC codes are power efficient. This paper aims to analyze the bit error rate (BER) performance of LDPC codes in MIMO-OFDM System for different modulation schemes. LDPC codes improve the BER performance of OFDM and MIMO-OFDM systems. MIMO-OFDM systems deliver better BER performance over OFDM system.
Volume: 14
Issue: 2
Page: 636-644
Publish at: 2025-08-01

Creating a smart bedroom for children by connecting PIR and LDR sensors to a microcontroller Arduino UNO ATmega328P

10.11591/ijict.v14i2.pp540-554
Ragmi M. Mustafa , Kujtim R. Mustafa , Refik Ramadani
Intelligent electronic systems are increasingly prevalent in modern society. The development of smart bedrooms for young children, especially those with developmental disabilities, it is based on the responses of passive infrared (PIR) and light dependent resistor (LDR) sensors. The PIR sensor detects children’s movement during the night, triggering the microcontroller to send a bit of 1 to the microcontroller pin connected to an electromagnetic relay, which then switches on a 220 VAC light to illuminate the bedroom. This only occurs if the LDR sensor has high resistance, indicating that the environment is completely dark. The functionality of this intelligent system mainly depends on the program code (sketch) uploaded to the Arduino UNO microcontroller module. The microcontroller is programmed to perform specific functions based on the sensors data. It is based on the responses of PIR and LDR sensors. The PIR sensor detects children’s movement during the night, triggering the microcontroller to send a bit of 1 to the microcontroller pin connected to an electromagnetic relay, which then switches on a 220 VAC light to illuminate the bedroom. This only occurs if the LDR sensor has high resistance, indicating that the environment is completely dark.
Volume: 14
Issue: 2
Page: 540-554
Publish at: 2025-08-01

Incremental prioritization using an iterative model for smallscale systems

10.11591/ijict.v14i2.pp565-574
Ameen Shaheen , Wael Alzyadat , Aysh Alhroob , A. Nasser Asfour
To improve customer satisfaction during the requirement engineering process and create higher consistency in the developed software, there is a growing trend toward the development and delivery of software in an incremental manner. This paper introduces a novel approach to prioritizing the initial development of core subsystems. This prioritization ensures that the most critical subsystems, which contribute significantly to the project’s overall success, are addressed first. Our method involves employing an incremental model with iterative modeling, where each subsystem is assigned a profitability score ranging from 1 to 10. The iterative model is then utilized to identify the most suitable subsystem for the next development stage. The results of our study indicate that utilizing the total profit weight in conjunction with the iterative model effectively identifies the central subsystem of the entire project. This approach proves to be the optimal starting point for development, helping streamline the process and contribute to a more efficient software delivery strategy.
Volume: 14
Issue: 2
Page: 565-574
Publish at: 2025-08-01

Design of a concurrent tri-band LNA based on composite right/left-handed transmission line resonators

10.12928/telkomnika.v23i4.26816
Faycal; Moulay Ismail University El Hardouzi , Mohammed; Moulay Ismail University Lahsaini , Mustapha; Moulay Ismail University Bahich , Badr; Moulay Ismail University Nasiri , Younes; Moulay Ismail University Achaoui
This article presents a three-band low noise amplifier (LNA) in microstrip technology based on composite right left handed transmmision line (CRLH TL) resonators for multi-band behavior, which has been designed to meet all the criteria that determine the quality of its operation. The transistor used is biased via a transmission line and matched by λ/4 transformer filters with CRLH-TL type resonators at the output to establish multiband behavior with improved band rejection to suppress unwanted frequencies and interference. The results demonstrate excellent performance at three frequencies: 900 MHz (15.03 dB gain), 2.1 GHz (13.58 dB gain), and 3.5 GHz (12.57 dB gain), with a noise figure below 2 dB and unconditional stability. The size of the proposed amplifier is 73×63 𝑚𝑚2 in area.
Volume: 23
Issue: 4
Page: 896-905
Publish at: 2025-08-01

Review of implantable-based wireless body area network metrics issues

10.11591/ijece.v15i4.pp4004-4021
Rawan Al Majdoubah , Yousef Eljaafreh
Recent developments in wireless communications, low-power integrated circuits, and biological physiological sensors have led to a new generation of wireless sensor networks. Body area networks are an interdisciplinary field that allows for real-time updates of medical records via the internet and continuous, affordable health monitoring. Several intelligent physiological sensors can be easily integrated into a flexible wireless body area network for implanted use, supporting early disease detection or computer-assisted rehabilitation. This field relies on the feasibility of small, easily implanted biosensors that do not impede daily activities. The body's implanted sensors record various physiological changes to monitor the patient's status no matter where they are. Nonetheless, because they handle health data, these networks ought to use benchmarking criteria to ensure high levels of service quality. Network routing protocols, wireless technologies, quality of service, privacy and security, energy efficiency, and performance are among the challenges being focused on to better satisfy its expectations. This review aims to comprehensively compare implantable wireless body area network metrics issues, seeking to generate a consistent and understandable overview. This study also attempts to address the gaps and provides a current assessment of the metrics concerning a wireless body area network used in healthcare services.
Volume: 15
Issue: 4
Page: 4004-4021
Publish at: 2025-08-01

High-speed field-programmable gate array implementation for mmWave orthogonal frequency-division multiplexing transmitters: design and evaluation

10.11591/ijece.v15i4.pp3813-3823
Kidsanapong Puntsri , Bussakorn Bunsri , Puripong Suthisopapan
This paper presents a field-programmable gate array (FPGA)-based implementation of an orthogonal frequency-division multiplexing (OFDM) transmitter signal processing chain optimized for high-speed millimeter wave (mmWave) communication systems. The design prioritizes real-time processing efficiency and flexibility. A high-throughput 2048-point inverse fast Fourier transform (IFFT) module, realized using a Radix-2 algorithm, forms the core of the design, showcasing efficient hardware resource utilization. The implementation further includes cyclic prefix (CP) insertion and configurable support for various quadrature amplitude modulation (QAM) modulation orders and pilot arrangements. The design is implemented in VHSIC Hardware Description Language (VHDL) using Vivado 2020 and evaluated on the Zynq UltraScale+ RFSoC ZCU111 evaluation kit. The processing pipeline employs eight parallel lanes for concurrent data computation. Experimental results demonstrate a mean squared error (MSE) of only 0.00013 between the FPGA-generated waveform and its MATLAB-simulated counterpart. Additionally, post-implementation resource utilization analysis shows efficient usage of FPGA resources. These findings validate the efficacy and real-time capability of the proposed FPGA-based OFDM transmitter leverages parallelism and high-speed architecture to efficiently process massive data streams, making it suitable for a wide range of mmWave OFDM applications. In contrast to recent works that focus on lower-order IFFT modules, this paper employs a high-throughput IFFT computation, showcasing efficient hardware resource utilization for high-speed mmWave applications.
Volume: 15
Issue: 4
Page: 3813-3823
Publish at: 2025-08-01

Indonesian speech emotion recognition: feature extraction and neural network approaches

10.11591/ijece.v15i4.pp3769-3778
Izza Nur Afifah , Tri Budi Santoso , Titon Dutono
This study explored the challenges of emotion recognition in Indonesian speech using deep learning techniques, addressing the complex nuances of emotional expression in spoken language that posed significant difficulties for automatic recognition systems. The research focused on the application of feature extraction methods and the implementation of convolutional neural networks (CNN) and a hybrid convolutional neural networks-long short-term memory (CNN-LSTM) model to identify emotional states from speech data. By analyzing key features of speech signals, including mel frequency cepstral coefficient (MFCC), zero crossing rate (ZCR), root mean square energy (RMSE), pitch, and spectral centroid, the study evaluated the models’ ability to capture both spatial and temporal patterns in the data. Testing was conducted using an Indonesian dataset comprising 200 samples. The CNN model, utilizing four features (MFCC, ZCR, RMSE, and pitch), and the CNN-LSTM model, which used three features (MFCC, ZCR, and RMSE), both achieved an emotion classification accuracy of approximately 88%. The result showed that the CNN-LSTM model achieved comparable performance with a simpler feature set compared to the CNN model. This highlighted the significance of choosing the appropriate techniques in feature extraction and classification to enhance the accuracy of identifying emotions from speech data while also managing computational complexity.
Volume: 15
Issue: 4
Page: 3769-3778
Publish at: 2025-08-01

Enhancing ultrasound image quality using deep structure of residual network

10.11591/ijece.v15i4.pp3779-3794
Ade Iriani Sapitri , Siti Nurmaini , Muhammad Naufal Rachmatullah , Annisa Darmawahyuni , Firdaus Firdaus , Anggun Islami , Bambang Tutuko , Akhiar Wista Arum
Ultrasonography, a medical imaging technique, is often affected by various types of noise and low brightness, which can result in low image quality. These drawbacks can significantly impede accurate interpretation and hinder effective medical diagnoses. Therefore, improving image quality is an essential aspect of the field of ultrasound systems. This study aims to enhance the quality of ultrasound images using deep learning (DL). The experiment is conducted using a custom dataset consisting of 2,175 infant heart ultrasound images collected from Indonesian hospitals, and the model is subsequently generalized using other datasets. We propose enhanced deep residual network combined convolutional neural networks (EDR-CNNs) to improve the image quality. After the enhancement process, our model achieved peak signal-to-noise ratio (PSNR) and structural similarity index metrics (SSIM) scores of 38.35 and 0.92 respectively, outperforming other methods. The benchmarking with other ultrasound medical images indicates that our proposed model produces good performance, as evidenced by higher PSNR, lower SSIM, a decrease in mean square error (MSE), and a lower contrast improvement index (CII). In conclusion, this study encapsulates the forthcoming trends in advancing low-illumination image enhancement, along with exploring the prevailing challenges and potential directions for further research.
Volume: 15
Issue: 4
Page: 3779-3794
Publish at: 2025-08-01
1 2 3
...
2097 2098
Show 250 of 2098

Discover Our Library

Embark on a journey through our expansive collection of articles and let curiosity lead your path to innovation.

Explore Now
Library 3D Ilustration