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International Journal of Reconfigurable and Embedded Systems (IJRES)
Published by Institute of Advanced Engineering and Science
ISSN : 20894864     EISSN : 27222608     DOI : -
Core Subject : Economy,
Economics, Econometrics & Finance
The centre of gravity of the computer industry is now moving from personal computing into embedded computing with the advent of VLSI system level integration and reconfigurable core in system-on-chip (SoC). Reconfigurable and Embedded systems are increasingly becoming a key technological component of all kinds of complex technical systems, ranging from audio-video-equipment, telephones, vehicles, toys, aircraft, medical diagnostics, pacemakers, climate control systems, manufacturing systems, intelligent power systems, security systems, to weapons etc. The aim of IJRES is to provide a vehicle for academics, industrial professionals, educators and policy makers working in the field to contribute and disseminate innovative and important new work on reconfigurable and embedded systems. The scope of the IJRES addresses the state of the art of all aspects of reconfigurable and embedded computing systems with emphasis on algorithms, circuits, systems, models, compilers, architectures, tools, design methodologies, test and applications.
Arjuna Subject : -
Articles 456 Documents
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Building a photonic neural network based on multi-operand multimode interference ring resonators Do, Thanh Tien; Pham, Hai Yen; Thanh, Trung
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 14, No 2: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v14.i2.pp311-319

Abstract

Photonic neural networks (PNNs) offer significant potential for enhancing deep learning networks, providing high-speed processing and low energy consumption. In this paper, we present a novel PNN architecture that employs nonlinear optical neurons using multi-operand 4×4 multimode interference (MMI) multi-operand ring resonators (MORRs) to efficiently perform vector dot-product calculations. This design is integrated into a photonic convolutional neural network (PCNN) with two convolutional layers and one fully connected layer. Simulation experiments, conducted using Lumerical and Ansys tools, demonstrated that the model achieved a high test accuracy of 98.26% on the MNIST dataset, with test losses stabilizing at approximately 0.04%. The proposed model was evaluated, demonstrating high computation speed, improved accuracy, low signal loss, and scalability. These findings highlight the model’s potential for advancing deep learning applications with more efficient hardware implementations.
Enhancing intrusion detection systems with hybrid HHO-WOA optimization and gradient boosting machine classifier Abualhaj, Mosleh M.; Abu-Shareha, Ahmad Adel; Rateb, Roqia
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 14, No 2: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v14.i2.pp518-526

Abstract

In this paper, we propose a hybrid intrusion detection system (IDS) that leverages Harris Hawks optimization (HHO) and whale optimization algorithm (WOA) for feature selection to enhance the detection of cyberattacks. The hybrid approach reduces the dimensionality of the NSL KDD dataset, allowing the IDS to operate more efficiently. The reduced feature set is then classified using logistic regression (LR) and gradient boosting machine (GBM) classifiers. Performance evaluation demonstrates that the GBM-HHO/WOA combination outperforms the LR-HHO/WOA approach, achieving an accuracy of 97.68%. These results indicate that integrating HHO and WOA significantly improves the IDS's ability to identify intrusions while maintaining high computational efficiency. This research highlights the potential of advanced optimization techniques to strengthen network security against evolving threats.
An approximate model SpMV on FPGA assisting HLS optimizations for low power and high performance Shaji, Alden C.; Aizaz, Zainab; Khare, Kavita
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 14, No 2: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v14.i2.pp375-387

Abstract

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.
Design and structural modelling of patient-specific 3D-printed knee femur and tibia implants Sandeep, Bolugoddu; Dhanushkodi, Saravanan; Kumarasamy, Sudhakar
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 14, No 2: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v14.i2.pp575-586

Abstract

Arthritis is a degenerative joint condition that progressively damages the knee, leading to pain, stiffness, and limited mobility. To alleviate these symptoms and restore joint functionality, total knee arthroplasty (TKA) is performed. This procedure becomes necessary due to either sudden trauma to the knee or gradual wear and tear of the meniscus and cartilage. TKA involves meticulous planning, precise bone cutting, and the placement of prosthetic components made from high-density polyethylene and metal alloys. However, traditional methods creating customized knee implants are expensive and time-intensive. This study explores the challenges in manufacturing personalized knee implants for TKA and evaluates the potential of three-dimensional (3D) printing technology in this process. Variations in knee joint anatomy across populations complicate surgery, as optimal outcomes rely on precise alignment and implant dimensions. A preoperative computed tomography (CT) scan identifies the region of interest (ROI), such as the knee joint. The scan data is then processed using computer-aided design (CAD) software to generate a printable file. The patient’s CT scan data is converted into a standard triangulation language (STL) file and CAD models of the knee joint. Errors such as overlapping triangles or open loops in the STL file are corrected, and unwanted geometries near the ROI are removed. Resection techniques are applied to create CAD models tailored to the patient’s bone morphology. Fused deposition modeling (FDM) is then used to produce prototypes of the knee joint and implants. Despite visible layer lines in the printed prototypes, challenges encountered during the process were effectively resolved.
A custom reduced instruction set computer-V based architecture for real-time electrocardiogram feature extraction Shinde, Vinayak Vikram; Bhandari, Sheetal Umesh; Khurge, Deepti Snehal; Nagarale, Satyashil Dasharath; Shirode, Ujwal Ramesh
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 14, No 2: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v14.i2.pp412-427

Abstract

The growing demand for energy-efficient and real-time biomedical signal processing in wearable devices has necessitated the development of application-specific and reconfigurable embedded hardware architectures. This paper presents the register transfer level (RTL) design and simulation of a custom reduced instruction set computer-V (RISC-V) based hardware architecture tailored for real-time electrocardiogram (ECG) feature extraction, focusing on R-peak detection and heart rate (HR) calculation. The proposed system combines ECG-specific functional blocks including a specialized ECG arithmetic logic unit and a finite state machine-based ECG control unit with a compact 16-bit RISC-V control core. Hardware optimized algorithms are used to carry out pre-processing activities such high-pass and low-pass filtering as well as feature extraction processes including moving average filtering, derivative calculation, and threshold based peak identification. Designed to reduce memory footprint and control complexity, a custom instruction set architecture supports modular reconfigurability. Functional validation is carried out by Xilinx Vivado simulating RTL components described in very high speed integrated circuit (VHSIC) hardware description language (VHDL). The present work shows successful simulation of important architectural components, complete system-level integration and custom ECG data validation. This work provides the basis for an application-specific, reconfigurable, power efficient hardware solution for embedded health-monitoring devices.
Enhanced fault detection in photovoltaic systems using an ensemble machine learning approach Ibrahim, Mohammed Salah; Almulla, Hussein k.; Sallibi, Anas D.; Nafea, Ahmed Adil; Kareem, Aythem Khairi; Alheeti, Khattab M. Ali
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 14, No 2: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v14.i2.pp507-517

Abstract

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.
Different methods of antenna reconfiguration by switches: a review Valsalam, Reji; Ramani, Perumal; Sharmila, Pandian
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 14, No 2: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v14.i2.pp301-310

Abstract

The rapid advancement of wireless communication technology has focused researcher's attention on reconfigurable antennas with multiple input and output (MIMO) and cognitive radio operation in high-data-rate modern wireless applications. Reconfigurable antennas perform various functions in terms of operating frequency, radiation pattern, and polarization. Electronic, mechanical, physical, and optical switches are used in reconfigurable antennas as control elements to adjust the switching mechanism and accomplish dynamic tuning. Electronic switches are the most widely used component in reconfigurable antennas because of their effectiveness, dependability, and simplicity in integrating with microwave circuitry. In this paper, a review of various kinds of efficient implementation methods for electrically controlled frequency reconfigurable antennas are proposed. More electrical switches are being used for reconfiguration such as micro electromechanical systems (MEMS), P-type, intrinsic, N-type (PIN), and varactor diodes. Even though PIN diodes are more frequently employed for reconfiguration due to their stability and constant variation in internal inductor and capacitor values. This study provides a deep analysis of the PIN diode usage in reconfigurable antennas and how to reduce the diodes in different microstrip reconfigurable antenna structures.
Optimizing social media analytics with the data quality enhancement and analytics framework for superior data quality Karthick, B.; Meyyappan, T.
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 14, No 2: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v14.i2.pp472-480

Abstract

his paper introduces the data quality enhancement and analytics (DQEA) framework to enhance data quality in social media analytics through machine learning (ML) algorithms. The efficacy of the framework is validated through features tested against human coders on Amazon Mechanical Turk, achieving an inter-coder reliability score of 0.85, indicating high agreement. Furthermore, two case studies with a large social media dataset from Tumblr were conducted to demonstrate the effectiveness of the proposed content features. In the first case study, the DQEA framework reduced data noise by 30% and bias by 25%, while increasing completeness by 20%. In the second case study, the framework improved data consistency by 35% and overall data quality score by 28%. Comparative analysis with state-of-the-art models, including random forest and support vector machines (SVM), showed significant improvements in data reliability and decision-making accuracy. Specifically, the DQEA framework outperformed the random forest model by 15% in accuracy and 20% in true positive rate, and the SVM model by 10% in error rate reduction and 18% in reliability. The results underscore the potential of advanced data analytics tools in transforming social media data into a valuable asset for organizations, highlighting the practical implications and future research directions in this domain.
Machine learning methods for energy sector in internet of things Hafezifard, Reyhane; Hosseini, Soodeh
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 14, No 2: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v14.i2.pp538-545

Abstract

This research paper focuses on exploring machine learning studies and conducting a comparative analysis of their advantages, disadvantages, implementation environments, and algorithms. A key aspect of the study involves evaluating the energy efficiency using machine learning algorithms to predict energy consumption. Additionally, a feature selection algorithm is employed to rank the features, with the highest-ranking feature identified as one of the most significant. The experimentation is conducted using the Weka tool, incorporating several machine learning algorithms such as linear regression, k-nearest neighbors, decision stump, radial basis function (RBF) network, and isotonic regression. The RBF algorithm, which relies on RBF, shares similarities with neural network algorithms. Results indicate a minimum error value of 1.546 for cooling load and 1.364 for heating load. The random forest algorithm emerges as the most suitable choice within the context of this study.
IoT-based smart agriculture system using fuzzy logic: case study in Vietnam Truong, Le Phuong; Thoi, Le Nam
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 14, No 2: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v14.i2.pp440-451

Abstract

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.

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