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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 23 Documents
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Search results for , issue "Vol 14, No 3: November 2025" : 23 Documents clear
Design and optimization of bail-shaped microstrip patch antenna for mid-band 5G application using a lightGBM model Vijayakumari, G.; Annalakshmi, T.
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 14, No 3: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v14.i3.pp626-637

Abstract

This study suggests a bail-shaped microstrip patch antenna designed for 5G applications. This antenna model operates in the 3.45 GHz wireless communication frequency range, which is a component of the so-called C-band (3.3 to 4.2 GHz), which is widely utilized for mid-band 5G deployments across the globe. Antenna size optimization is achieved at 31×28 mm2. On the patch, a slot is added to enhance the return loss features. The light gradient boosting machine (LightGBM) model for prediction acts as an objective function of the considered piranha foraging optimization algorithm (PFOA) to adjust the antenna's slot dimension, which will be used to optimize the slot width. In order to get a superior return loss value of around -39.90<-10 dB, the optimization approach that is provided seeks to achieve the ideal slot length. The proposed device exhibits remarkable radiation efficiency by partially grounding, with a peak gain of around 2.535 dBi at 3.45 GHz. A novel hybrid approach combines the LightGBM prediction model with the PFOA to fine-tune slot dimensions, achieving a superior return loss of -39.90 dB. The exclusivity of this effort is the incorporation of machine learning algorithms to attain significantly improved parameters.
Chirp-pulsed eddy current testing for crack detection in low-carbon steel Le, Dang-Khanh; Phuong Hoang, Sy; Minh Le, Duc; Huy Pham, Phuong; Hieu Trieu, Trung; Le, Minhhuy
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 14, No 3: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v14.i3.pp676-686

Abstract

This paper introduces a signal processing feature for chirp-pulsed eddy current testing (C-PECT) to improve crack detection in low-carbon steel, a common material in maritime structures. While C-PECT is an established technique, inspecting ferromagnetic materials is challenging due to significant background noise from lift-off variations and material permeability. The novelty of this work lies in the proposal of a frequency-domain integration feature designed to suppress this noise. The method utilizes a chirp-pulse-excited probe with a Hall sensor to measure the magnetic field response. By integrating the signal's magnitude spectrum, the frequency feature effectively flattens the background and enhances the signal-to-noise ratio. Experimental validation on a low-carbon steel specimen with artificial cracks demonstrates the feature's superior performance in providing clear, high-contrast crack indications compared to a conventional time-domain analysis. The results indicate that this approach offers a simple, computationally efficient, and robust solution for the qualitative detection and localization of cracks, enhancing structural integrity assessments in noisy industrial environments.
Calibration and measurement of cotton moisture using real time system with statistical analysis Pundlikrao Jungare, Suyog; V. Joshi, Prasad; K. Sharma, M.
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 14, No 3: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v14.i3.pp687-695

Abstract

Accurate moisture measurement in cotton is essential for maintaining fibre quality, ensuring safe storage, and supporting efficient processing. Improper moisture levels can result in microbial growth, fibre degradation, or mechanical damage during ginning and spinning operations. This study presents the development of a real-time moisture measurement system for cotton used in the ginning industry. The system operates on the principle of electrical resistance change to detect varying moisture levels. Cotton samples were categorized into four types: wet, new, old, and dry. The system is designed for use on moving or in-process cotton. To evaluate system performance, linear discriminant analysis (LDA), and hierarchical clustering analysis (HCA) were employed for classification. Partial least squares (PLS) regression was used to calibrate the system against the standard oven-drying method (ASTM D2495-07). Further, artificial neural network (ANN) modelling was applied for moisture prediction. The system successfully discriminated between the cotton types, achieving over 85% explained variance in classification. ANN-based prediction aligned closely with the standard reference method. The developed system provides a low-cost, fast, and real-time solution for moisture measurement in cotton, with strong potential for industrial application.

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