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28,451 Article Results

Arowana cultivation water quality forecasting with multivariate fuzzy timeseries and internet of things

10.11591/csit.v6i2.p136-146
Alauddin Maulana Hirzan , April Firman Daru , Lenny Margaretta Huizen
Water quality plays a crucial role in the growth and survival of arowana fish, with imbalances in key parameters (pH, temperature, turbidity, dissolved oxygen, and conductivity) leading to increased mortality rates. While previous studies have introduced various monitoring models using Arduino IDE and intrinsic approaches, they lack predictive capabilities, leaving cultivators unable to take proactive measures. To address this gap, this study develops a predictive model integrating the internet of things (IoT) with a fuzzy time series (FTS) algorithm. Through rigorous evaluation and validation, the proposed FTS-multivariate T2 model demonstrated superior performance, achieving an exceptionally low error rate of 0.01704%, outperforming decision tree (0.13410%), FTS-multivariate T1 (0.88397%), and linear regression (20.91791%). These findings confirm that FTS-multivariate T2 not only accurately predicts water quality but also significantly reduces the mean absolute percentage error, providing a robust solution for sustainable arowana aquaculture.
Volume: 6
Issue: 2
Page: 136-146
Publish at: 2025-07-01

Test and measurement automation of printed circuit board assembly using digital oscilloscope

10.11591/ijres.v14.i2.pp463-471
Sanjeev Kumar , Deepak Prasad , Manoj Pandey
The testing and measurement (TM) of electrical parameters of printed circuit board assembly (PCBA) plays an integral part in the manufacturing sectors. These industries work on embedded system which widely use digital oscilloscopes (DO) for such purposes, however, operate them manually. An exponential rise in the implementation of industry 4.0 with the increasing demand for industrial products makes manual TM cumbersome. The automation of oscilloscopes (AO) remains a viable alternative to these issues requiring further investigation. An accurate and automated TM block facilitates efficient design, development, and assembly of a fully functional system hence addressed here. The AO has been carried out using generalized software that can be configured based on industry requirements. It subsequently stores the data on the server for better traceability. The automated software is developed using VB.NET and installed on a personal computer. Experiments reveal the proposed approach saves approximately 60%-70% of the time required for each PCBA operation than that of the manual system. This can enhance the productivity of the industry in terms of manpower and Resource utilization with a reduction in operating costs.
Volume: 14
Issue: 2
Page: 463-471
Publish at: 2025-07-01

Design of a dual-band bandpass filter with shunt stubs for wireless local area network and satellite communication system

10.11591/ijres.v14.i2.pp490-496
Jacob Abraham , Kannadhasan Suriyan
High-performance radio frequency (RF) modules are required in transmitter and reception devices due to the recent expansion of wireless technology. The power amplifier, low-noise amplifier, filter, and mixer are the most crucial components in the RF transmitter/receiver chain. This work presents the design and analysis of a dual-band bandpass filter (BPF) for wireless local area network (WLAN) and C-band satellite applications. Stubs of the proper electrical length that are open and short-circuited are used to implement the filter. The low pass performance is generated by the open-circuited stubs. Short-circuited stubs achieve high-pass performance, while the combination of open and short-circuited stubs achieves bandpass performance. We confirm the filter's behaviour using the advanced design system 2022 simulation tool. The findings of return loss and insertion loss confirm the simulation-level performance analysis of the filter. The result demonstrates the suggested BPF's dual-band behaviour at 4 GHz and 6 GHz.
Volume: 14
Issue: 2
Page: 490-496
Publish at: 2025-07-01

Design and evaluation of clock-gating-based approximate multiplier for error-tolerant applications

10.11591/ijres.v14.i2.pp398-411
Chowdam Venkata Sudhakar , Suresh Babu Potladurty , Prasad Reddy Karipireddy
The multiplier is an essential component in real-time applications. Even though approximation arithmetic affects output accuracy in multipliers, it offers a realistic avenue to constructing power area and speed-efficient digital circuits. The approximation computing technique is commonly used in error-tolerant applications such as signal, image, and video processing. In this paper, approximate multipliers (AMs) are designed using both conventional and approximate half adders (A-HA) and full adders (A-FA), which are strategically placed to add partial products at the most significant bit (MSB) positions, and OR gates are used to add partial products at the lower significant bit (LSB). In addition, this research article demonstrates unsigned and signed multipliers using the ripple carry adder (RCA), carry save adder (CSA), conditional sum adder (COSA), carry select adder (CSLA), and clock gating technique. The proposed multipliers are implemented in Verilog hardware description language (HDL) and simulated on the Xilinx VIVADO 2021.2 design tool with target platform Artix-7 AC701 FPGA. The simulation results found that unsigned and signed approximate multiplier power consumption was reduced by 13% and 18.18% respectively and enhanced accuracy.
Volume: 14
Issue: 2
Page: 398-411
Publish at: 2025-07-01

Design and development of multiband multi-mode frequency reconfigurable CPW-fed antenna for 5G wireless communication

10.11591/ijres.v14.i2.pp328-338
Annu Tiwari , Muhammed Yasir Yilmaz , Gaurav Kumar Soni , Dinesh Yadav
This research develops, simulates, fabricates and measured a coplanar waveguide (CPW)-fed multiband multi-mode frequency reconfigurable antenna for 5G wireless communication. The antenna is design on Rogers RT5880 substrate with a dielectric constant of 2.2, a thickness of 0.508 mm, and a loss tangent (tanδ) of 0.0009 and the dimension is 30×28×0.508 mm3. The presented antenna has shown good impedance matching with reflection coefficients ranging from -14.82 to -50.36 dB at different frequencies between 6 GHz to 24 GHz. The presented frequency reconfigurable antenna design includes four PIN diodes, resistors, and inductors, enabling 16 different configurations. The simulated outcomes showed varied S parameter values and gains, demonstrating the antenna's flexibility. Measurements were taken using vector network analyzer (VNA) and anechoic chamber to assess reflection coefficient (|S11|) and gain, confirming the antenna's performance. The antenna's ability to reconfigure dynamically without losing signal integrity makes it suitable for 5G wireless applications. It meets and exceeds the requirements for multiband operation, validated by comprehensive simulations and measurements, showing its potential for wide use.
Volume: 14
Issue: 2
Page: 328-338
Publish at: 2025-07-01

Investigating the performance of RNN model to forecast the electricity power consumption in Guangzhou China

10.11591/ijres.v14.i2.pp497-506
Han Mingying , Azman Ab Malik , Noormadinah Allias , Irni Hamiza binti Hamzah
The project initiatives to create a reliable prediction model for power loads in Guangzhou, China. The power industry is facing issues due to rapid market growth and the necessity for better grid management, prompting this response. In developing the models, conventional machine learning models have been used so far, but in this study, the performance of deep learning is investigated. Therefore, the recurrent neural network (RNN) was selected for the prediction of electricity consumption. Later, the performance of the model was compared with autoregressive integrated moving average (ARIMA), long short-term memory (LSTM), and RNN. The experimental results show that the RNN outperforms ARIMA and LSTM, with an R² value of 0.92, an RMSE of 0.13107 and an MAE of 0.0176. The project improved power resource planning and management, selected an acceptable forecasting model RNN and contributed to forecasting technology developments. The study identified limits in historical data availability and quality, as well as external influences affecting the studies. RNN models can help optimize resource allocation and improve energy planning.
Volume: 14
Issue: 2
Page: 497-506
Publish at: 2025-07-01

Enhancing scalability and efficiency in technological transaction utilizing dual-layer blockchain approach

10.11591/ijres.v14.i2.pp452-462
T. Kanimozhi , M. Inbavalli
The leather industry encounters significant challenges in integrating blockchain technology and smart contracts into its complex supply networks. Despite technological advancements, existing supply chain management systems suffer from inefficiencies, opacity, and vulnerabilities to fraud. Blockchain offers promising solutions such as immutable ledgers, decentralized governance, and smart contract automation. However, scalability limitations hinder the efficient handling of high transaction volumes, impacting procurement, production, inventory management, and distribution processes, leading to delays and increased costs. This research aims to address these challenges by exploring innovative approaches, including dual-layer blockchain architectures incorporating sharding and state channels, tailored to the unique needs of the leather industry. By overcoming scalability barriers, the research seeks to unlock the transformative potential of blockchain technology and smart contracts, enhancing transparency, traceability, and efficiency in leather supply chains while ensuring global interoperability and regulatory compliance. Through empirical validation and comparative analysis, this study provides understandings into the practical implementation of blockchain solutions within the leather industry, offering strategic guidance for sustainable supply chain management practices.
Volume: 14
Issue: 2
Page: 452-462
Publish at: 2025-07-01

Pipelined reconfigurable architecture for 5G software-defined radio systems

10.11591/ijres.v14.i2.pp320-327
Vijaya Bhaskar Chalampalem , Munaswamy Pidugu , Sanacarapu Nagaraju
The filters are used to allow a specific band of frequencies. In a wireless communication, the filter is used to select the frequency of operation with a narrow or broad band. As the generations increase the amount of data handled increases drastically. 5G data rate can be significantly deliver up to 20 Gigabits per second while 4G communication data rate is handled in the order of 100 Megabits per second. Now the challenge becomes processing data at such a speed with low power and low area specifications. The filters that can configure themselves as per the data received are reconfigurable filters so that the bandwidth is saved. Also, when the pipelining is introduced, the reconfigurable filter improves the performance of the design. This paper details about the pipelined reconfigurable finite impulse response (RFIR) filter with the simplest algorithm with auto updating capability. The design is modelled in Verilog hardware description language (HDL) language, synthesized for Cyclone III field-programmable gate array (FPGA). The results prove that the proposed filter increases only slightly with respect to delay and power dissipation with a trade off in area and maximum possible clock frequency.
Volume: 14
Issue: 2
Page: 320-327
Publish at: 2025-07-01

Blockchain-based decentralized voting system with SHA-256 algorithm and facial recognition

10.11591/ijres.v14.i2.pp481-489
BJD Kalyani , Jaya Krishna Modadugu , Sarabu Neelima
Blockchain technology has completely changed the way data is stored and transactions are verified. It provides a dependable, transparent, and safe medium for communication and transaction validation. In order to solve the drawbacks of conventional electronic voting systems, the goal of this research project is to design a decentralized voting system based on blockchain technology. The suggested method offers an immutable and safe way to record and validate votes by utilizing the security and transparency capabilities of blockchain technology. The suggested approach provides an immutable and safe way to record and validate votes by utilizing the security and transparency capabilities of blockchain technology. This paper aims to provide a comprehensive process for digital identity authentication, create a voter interface that is compatible with Ethereum wallets, and apply smart contracts on the Ethereum network to speed up voter registration, ballot preparation, voting, and result tabulation. Additionally, this paper proposes to build up a multi-factor authentication system for election managers and validators to offer them safe and approved power over the voting process. By carefully examining the existing methods, this research highlights the flaws and weaknesses of traditional electronic voting systems and stresses the need for more trustworthy and secure voting technology. The proposed blockchain-based voting system offers an innovative solution to problems with voter fraud and election manipulation because of its irreversible blockchain record, which gives a high degree of transparency and integrity.
Volume: 14
Issue: 2
Page: 481-489
Publish at: 2025-07-01

Blockchain technology for optimizing security and privacy in distributed systems

10.11591/csit.v6i2.p214-224
Wisnu Uriawan , Adrian Putra Pratama , Shafwan Mursyid
Blockchain technology is increasingly recognized as an effective solution for addressing security and privacy challenges in distributed systems. Blockchain ensures information security by validating data and defending against cyber threats, while guaranteeing data integrity through transaction validation and reliable storage. The research involves a literature study, problem identification, analysis of blockchain security and privacy, model development, testing, and analysis of trial results. Furthermore, blockchain enables user anonymity and fosters transparency by utilizing a distributed network, reducing the risk of fraudulent activities. Its decentralized nature ensures high reliability and accessibility, even in node failures. Blockchain enhances security and privacy by offering features like data immutability, provenance, and reduced reliance on trust. It decentralizes data storage, making tampering or deletion extremely challenging, and ensures the invalidation of subsequent blocks upon any changes. Blockchain finds applications in various domains, including supply chains, finance, healthcare, and government, enabling enhanced security by tracking data origin and ownership. Despite scalability and security challenges, the potential benefits of reduced costs, increased efficiency, and improved transparency position blockchain as a promising technology for the future. In summary, blockchain technology provides secure transaction recording and data storage, thus enhancing security, privacy, and the integrity of sensitive information in distributed systems.
Volume: 6
Issue: 2
Page: 214-224
Publish at: 2025-07-01

Exploring the landscape of approximate subtraction methods in ASIC platform

10.11591/ijres.v14.i2.pp388-397
M. Priyadharshni , Rajermani Thinakaran , Grande Naga Jyothi , Vijayakumar Varadarajan , C. Srinivasa Murthy
Approximate computing has emerged as a crucial technique in modern computing, offering significant benefits for error-resilient applications. Error resilient applications include signal, image, audio processing, and multimedia. These applications will accept the errored results with some degree of tolerance. This approach allows these applications to process and embrace data that may deviate slightly from perfect accuracy. The utility of approximate computing extends to both hardware and software domains. In hardware, arithmetic units are particularly important, among that approximate subtractors have gained attention for their role in these units. A comparative study was conducted on various approximate subtractors from existing literature, considering structural analysis in all scenarios. These approximate subtractors are coded in Verilog hardware description language (HDL) and synthesized in Synopsys electronic design automation (EDA) Tool using Taiwan Semiconductor Manufacturing Company (TSMC) 65 nm technology. Out of the available choices, approximate subtractor 3 is particularly well-suited for processing higher bit data due to its reduced hardware complexity and minimal error. Notably, it outperforms exact subtractors by achieving a notable reduction of 20% in the area delay product (ADP) and 15% in the power delay product (PDP) as process innovation. These improvements highlight the efficiency and effectiveness of approximate subtractor 3, making it a compelling option for various computing applications which accept the inaccurate results.
Volume: 14
Issue: 2
Page: 388-397
Publish at: 2025-07-01

Classifying IoT firmware security threats using image analysis and deep learning

10.11591/ijres.v14.i2.pp546-557
Abdelkabir Rouagubi , Chaymae El Youssofi , Khalid Chougdali
As the internet of things (IoT) grows, its embedded devices face increasing vulnerability to firmware-based attacks. The lack of robust security mechanisms in IoT devices makes them susceptible to malicious firmware updates, potentially compromising entire networks. This study addresses the classification of IoT firmware security threats using deep learning and image-based analysis techniques. A publicly available dataset of 32×32 grayscale images, derived from IoT firmware samples and categorized as benignware, hackware, and malware, was utilized. The grayscale images were converted into three-channel RGB format to ensure compatibility with convolutional neural networks (CNNs). We tested multiple pre-trained CNN architectures, including SqueezeNet, ShuffleNet, MobileNet, Xception, and ResNet50, employing transfer learning to adapt the models for this classification task. Both ResNet50 and ShuffleNet achieved exceptional performance, with 100% accuracy, precision, recall, and F1-score. These results validate the effectiveness of our methodology in leveraging transfer learning for IoT firmware classification while maintaining computational efficiency, making it suitable for deployment in resource-constrained IoT environments. T
Volume: 14
Issue: 2
Page: 546-557
Publish at: 2025-07-01

IoT-based smart agriculture system using fuzzy logic: case study in Vietnam

10.11591/ijres.v14.i2.pp440-451
Le Phuong Truong , Le Nam Thoi
This paper presents an internet of things (IoT)-based smart agriculture system using fuzzy logic. This system automatically supervise and regulate pivotal parameters like temperature, humidity, pH, nutrients (NPK), and electrical conductivity (Ec) for vegetables. Data from the cultivation environment is gathered by sensors system and processed by fuzzy logic algorithms to make appropriate control decisions, ensuring optimal crop growth conditions. Additionally, a web application was developed to monitor temperature, humidity, Ec, pH, and NPK content. Moreover, when any of the NPK, Ec, pH, temperature or humidity indices fall outside allowed ranges, the system send warning notifications through the web application. Furthermore, an IP camera was installed to take images of the garden and send them to users via this web app. Experimental results demonstrate the system's reliability with a pH root mean square error (RMSE) of 0.22 and temperature RMSE of 0.93, corresponding to low errors of 0.034% and 0.056% respectively. Concurrently, this system optimizes resource utilization including water and electricity to assist in reducing production costs.
Volume: 14
Issue: 2
Page: 440-451
Publish at: 2025-07-01

Enhanced fault detection in photovoltaic systems using an ensemble machine learning approach

10.11591/ijres.v14.i2.pp507-517
Mohammed Salah Ibrahim , Hussein k. Almulla , Anas D. Sallibi , Ahmed Adil Nafea , Aythem Khairi Kareem , Khattab M. Ali Alheeti
Malfunctioning of photovoltaic (PV) systems is a main issue affecting solar panels and other related components. Detecting such issues early leads to efficient energy production with low maintenance costs and high system performance consistency. This paper proposed an ensemble model (EM) for fault detection (FD) in PV systems. The proposed model utilized advanced machine learning algorithms containing random forest (RF), k-nearest neighbors (KNN), and gradient boosting (GB). Traditional approaches often do not handle the several situations that PV systems can have. Our EM leveraged the power of GB’s algorithm in handling complex data patterns through iterative boosting, KNN’s capability in capturing local data structures, and RF’s strength in handling overfitting and noise through its tree structure randomness. Combining these models enhanced fault detection capabilities, providing excellent accuracy compared to individual models. To evaluate the performance of our EM, different experiments were conducted. The results demonstrated substantial improvements in detection fault, achieving an accuracy rate of 95%. This accuracy rate considered high underscores the model’s capability to handle fault detection of PV systems, posing a consistent solution for instant fault detection and maintenance scheduling.
Volume: 14
Issue: 2
Page: 507-517
Publish at: 2025-07-01

An approximate model SpMV on FPGA assisting HLS optimizations for low power and high performance

10.11591/ijres.v14.i2.pp375-387
Alden C. Shaji , Zainab Aizaz , Kavita Khare
High performance computing (HPC) in embedded systems is particularly relevant with the rise of artificial intelligence (AI) and machine learning at the edge. Deep learning models require substantial computational power, and running these models on embedded systems with limited resources poses significant challenges. The energy-efficient nature of field-programmable gate arrays (FPGAs), coupled with their adaptability, positions them as compelling choices for optimizing the performance of sparse matrix-vector multiplication (SpMV), which plays a significant role in various computational tasks within these fields. This article initially did analysis to find a power and delay efficient SpMV model kernel using high level synthesis (HLS) optimizations which incorporates loop pipelining, varied memory access patterns, and data partitioning strategies, all of this exert influence on the underlying hardware architecture. After identifying the minimum resource utilization model, we propose an approximate model algorithm on SpMV kernel to reduce the execution time in Xilinx Zynq-7000 FPGA. The experimental results shows that the FPGA power consumption was reduced by 50% when compared to a previously implemented streaming dataflow engine (SDE) flow, and the proposed approximate model improved performance by 2× times compared to that of original compressed sparse row (CSR) sparse matrix.
Volume: 14
Issue: 2
Page: 375-387
Publish at: 2025-07-01
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