cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
-
Editorial Address
-
Location
Kota yogyakarta,
Daerah istimewa yogyakarta
INDONESIA
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 26 Documents
 1   2   3 
Search results for , issue "Vol 13, No 1: March 2024" : 26 Documents clear
Accurate plant species analysis for plant classification using convolutional neural network architecture Patil, Savitha; Sasikala, Mungamuri
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v13.i1.pp160-170

Abstract

Recently, plant identification has become an active trend due to encouraging results achieved in plant species detection and plant classification fields among numerous available plants using deep learning methods. Therefore, plant classification analysis is performed in this work to address the problem of accurate plant species detection in the presence of multiple leaves together, flowers, and noise. Thus, a convolutional neural network based deep feature learning and classification (CNN-DFLC) model is designed to analyze patterns of plant leaves and perform classification using generated fine-grained feature weights. The proposed CNN-DFLC model precisely estimates which the given image belongs to which plant species. Several layers and blocks are utilized to design the proposed CNN-DFLC model. Fine-grained feature weights are obtained using convolutional and pooling layers. The obtained feature maps in training are utilized to predict labels and model performance is tested on the Vietnam plant image (VPN-200) dataset. This dataset consists of a total number of 20,000 images and testing results are achieved in terms of classification accuracy, precision, recall, and other performance metrics. The mean classification accuracy obtained using the proposed CNN-DFLC model is 96.42% considering all 200 classes from the VPN-200 dataset.
Design and development of control and monitoring hydroponic system Mujtahidin, Muhammad Hanafi; Shah, Ahmad Feirdaous Mohd; Jais, Ahmadul Sayyidi Amin; Annuar, Khalil Azha Mohd; Sapiee, Mohd Razali Mohamad
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v13.i1.pp41-51

Abstract

The global agriculture system faces significant challenges in meeting the growing demand for food production, particularly given projections that the world's population will reach 70% by 2050. Hydroponic farming is an increasingly popular technique in this field, offering a promising solution to these challenges. This paper will present the improvement of the current traditional hydroponic method by providing a system that can be used to monitor and control the important element in order to help the plant grow up smoothly. This proposed system is quite efficient and user-friendly that can be used by anyone. This is a combination of a traditional hydroponic system, an automatic control system and a smartphone. The primary objective is to develop a smart system capable of monitoring and controlling potential hydrogen (pH) levels, a key factor that affects hydroponic plant growth. Ultimately, this paper offers an alternative approach to address the challenges of the existing agricultural system and promote the production of clean, disease-free, and healthy food for a better future.
Implementing hue-saturation-value filter and circle hough transform for object tracking on ball-wheeled robot Sugiarto, Kharis; Kusuma, Vicky Andria; Firdaus, Aji Akbar; Suprapto, Sena Sukmananda; Putra, Dimas Fajar Uman
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v13.i1.pp52-58

Abstract

The ball-wheeled robot relies on a camera for receiving information on the object to be followed. Object tracing is one of the methods that can be used for detecting object movement. In recognizing objects around it, the robot requires an image analysis process that involves visual perception. Image processing is the process of processing and analyzing images that involves visual perception, and is characterized by input data and output information in the form of images. This is how the robot can see objects around it and then be assisted by computer vision to make a decision. The object tracking method with hue-saturation-value (HSV) colour filtering and shape recognition with circle hough transform (CHT) is applied to the ball-wheeled robot. The front vision of the robot uses HSV colour filtering with various test values to determine the thresholding value, and it was found that the ball could be identified up to a distance of 1,000 cm. To further improve the performance of recognizing the ball object, CHT was applied. It was found that the ball could be identified up to a distance of 700 cm. Furthermore, the ball can be identified in obstructed conditions up to 75%.
Affordable digital electronics for building a hybrid dynamic marker structure with infrared illumination light patterns Serrano-Pérez, Edgar; Soberanes-Martín, Anabelem
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v13.i1.pp20-24

Abstract

This work deals with the integration of low-cost electronic devices that were integrated into constructing a dynamic maker that allows the triggering of augmented reality events. A hybrid structure was developed to combine the most favorable aspects of fiducial markers and dynamic markers. The lighting infrared patterns are effectively modifiable through the programming of an ESP8266 microcontroller card. To test the system, an infrared lighting pattern generated was detected through a digital camera, and an augmented reality application was implemented using a web page for displaying text. Electronic shift registers were used for the temporal storage of the infrared illumination pattern. The infrared illumination marker can’t be detected by human eyes, but it is easily recognized due to the inner black square shape embedded into a white wooden structure.
Design of Arduino UNO based smart irrigation system for real time applications Ramasamy, Palanisamy; Pandian, Nagarajan; Mayathevar, Krishnamurthy; Ravindran, Ramkumar; Kandula, Srinivasa Rao; Devadoss, Selvabharathi; Kuppusamy, Selvakumar
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v13.i1.pp105-110

Abstract

The fundamental principle of the paper is that the soil moisture sensor obtains the moisture content level of the soil sample. The water pump is automatically activated if the moisture content is insufficient, which causes water to flow into the soil. The water pump is immediately turned off when the moisture content is high enough. Smart home, smart city, smart transportation, and smart farming are just a few of the new intelligent ideas that internet of things (IoT) includes. The goal of this method is to increase productivity and decrease manual labour among farmers. In this paper, we present a system for monitoring and regulating water flow that employs a soil moisture sensor to keep track of soil moisture content as well as the land’s water level to keep track of and regulate the amount of water supplied to the plant. The device also includes an automated led lighting system.
Innovative systems for the detection of air particles in the quarries of the Western Rif, Morocco Fattah, Ghizlane; Mabrouki, Jamal; Ghrissi, Fouzia; Elouardi, Mohamed
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v13.i1.pp117-125

Abstract

In a world where climate change looms large the spotlight often shines on greenhouse gases, but the shadow of man-made aerosols should not be underestimated. These tiny particles play a pivotal role in disrupting Earth's radiative equilibrium, yet many mysteries surround their influence on various physical aspects of our planet. The root of these mysteries lies in the limited data we have on aerosol sources, formation processes, conversion dynamics, and collection methods. Aerosols, composed of particulate matter (PM), sulfates, and nitrates, hold significant sway across the hemisphere. Accurate measurement demands the refinement of in-situ, satellite, and ground-based techniques. As aerosols interact intricately with the environment, their full impact remains an enigma. Enter a groundbreaking study in Morocco that dared to compare an internet of thing (IoT) system with satellite-based atmospheric models, with a focus on fine particles below 10 and 2.5 micrometers in diameter. The initial results, particularly in regions abundant with extraction pits, shed light on the IoT system's potential to decode aerosols' role in the grand narrative of climate change. These findings inspire hope as we confront the formidable global challenge of climate change.
AnoMalNet: outlier detection based malaria cell image classification method leveraging deep autoencoder Huq, Aminul; Reza, Md Tanzim; Hossain, Shahriar; Dipto, Shakib Mahmud
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v13.i1.pp171-178

Abstract

Class imbalance is a pervasive issue in the field of disease classification from medical images. It is necessary to balance out the class distribution while training a model. However, in the case of rare medical diseases, images from affected patients are much harder to come by compared to images from non-affected patients, resulting in unwanted class imbalance. Various processes of tackling class imbalance issues have been explored so far, each having its fair share of drawbacks. In this research, we propose an outlier detection based image classification technique which can handle even the most extreme case of class imbalance. We have utilized a dataset of malaria parasitized and uninfected cells. An autoencoder model titled AnoMalNet is trained with only the uninfected cell images at the beginning and then used to classify both the affected and non-affected cell images by thresholding a loss value. We have achieved an accuracy, precision, recall, and F1 score of 98.49%, 97.07%, 100%, and 98.52% respectively, performing better than large deep learning models and other published works. As our proposed approach can provide competitive results without needing the disease-positive samples during training, it should prove to be useful in binary disease classification on imbalanced datasets.
Deep convolutional neural network framework with multi-modal fusion for Alzheimer’s detection Sharma, Manoj Kumar; Kaiser, M. Shamim; Ray, Kanad
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v13.i1.pp179-191

Abstract

The biomedical profession has gained importance due to the rapid and accurate diagnosis of clinical patients using computer-aided diagnosis (CAD) tools. The diagnosis and treatment of Alzheimer’s disease (AD) using complementary multimodalities can improve the quality of life and mental state of patients. In this study, we integrated a lightweight custom convolutional neural network (CNN) model and nature-inspired optimization techniques to enhance the performance, robustness, and stability of progress detection in AD. A multi-modal fusion database approach was implemented, including positron emission tomography (PET) and magnetic resonance imaging (MRI) datasets, to create a fused database. We compared the performance of custom and pre-trained deep learning models with and without optimization and found that employing natureinspired algorithms like the particle swarm optimization algorithm (PSO) algorithm significantly improved system performance. The proposed methodology, which includes a fused multimodality database and optimization strategy, improved performance metrics such as training, validation, test accuracy, precision, and recall. Furthermore, PSO was found to improve the performance of pre-trained models by 3-5% and custom models by up to 22%. Combining different medical imaging modalities improved the overall model performance by 2-5%. In conclusion, a customized lightweight CNN model and nature-inspired optimization techniques can significantly enhance progress detection, leading to better biomedical research and patient care.
An approach to diagnosis of prostate cancer using fuzzy logic Rawat, Meena; Pathak, Pooja; Vats, Pooja
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v13.i1.pp192-200

Abstract

Early diagnosis of cancers is a major requirement for patients and a complicated job for the oncologist. If it is diagnosed early, it could have made the patient more likely to live. For a few decades, fuzzy logic emerged as an emphatic technique in the identification of diseases like different types of cancers. The recognition of cancer diseases mostly operated with inexactness, inaccuracy, and vagueness. This paper aims to design the fuzzy expert system (FES) and its implementation for the detection of prostate cancer. Specifically, prostate-specific antigen (PSA), prostate volume (PV), age, and percentage free PSA (%FPSA) are used to determine prostate cancer risk (PCR), while PCR serves as an output parameter. Mamdani fuzzy inference method is used to calculate a range of PCR. The system provides a scale of risk of prostate cancer and clears the path for the oncologist to determine whether their patients need a biopsy. This system is fast as it requires minimum calculation and hence comparatively less time which reduces mortality and morbidity and is more reliable than other economic systems and can be frequently used by doctors.
Design and build an airbag system for elderly fall protection using the MPU6050 sensor module Suprapto, Sena Sukmananda; Kusuma, Vicky Andria; Firdaus, Aji Akbar; Putra, Wahyu Haryanto; Yuniar, Risty Jayanti
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v13.i1.pp111-116

Abstract

The use of technology has a significant impact to reduce the consequences of accidents. Sensors, small components that detect interactions experienced by various components, play a crucial role in this regard. This study focuses on how the MPU6050 sensor module can be used to detect the movement of people who are falling, defined as the inability of the lower body, including the hips and feet, to support the body effectively. An airbag system is proposed to reduce the impact of a fall. The data processing method in this study involves the use of a threshold value to identify falling motion. The results of the study have identified a threshold value for falling motion, including an acceleration relative (AR) value of less than or equal to 0.38 g, an angle slope of more than or equal to 40 degrees, and an angular velocity of more than or equal to 30 °/s. The airbag system is designed to inflate faster than the time of impact, with a gas flow rate of 0.04876 m3 /s and an inflating time of 0.05 s. The overall system has a specificity value of 100%, a sensitivity of 85%, and an accuracy of 94%.

Page 2 of 3 | Total Record : 26

 1   2   3 

Filter by Year

2024 2024


Reset
Filter By Issues
All Issue Vol 14, No 3: November 2025 Vol 14, No 2: July 2025 Vol 14, No 1: March 2025 Vol 13, No 3: November 2024 Vol 13, No 2: July 2024 Vol 13, No 1: March 2024 Vol 12, No 3: November 2023 Vol 12, No 2: July 2023 Vol 12, No 1: March 2023 Vol 11, No 2: July 2022 Vol 11, No 1: March 2022 Vol 10, No 3: November 2021 Vol 10, No 2: July 2021 Vol 10, No 1: March 2021 Vol 9, No 3: November 2020 Vol 9, No 2: July 2020 Vol 9, No 1: March 2020 Vol 8, No 3: November 2019 Vol 8, No 2: July 2019 Vol 8, No 1: March 2019 Vol 7, No 3: November 2018 Vol 7, No 2: July 2018 Vol 7, No 1: March 2018 Vol 6, No 3: November 2017 Vol 6, No 2: July 2017 Vol 6, No 1: March 2017 Vol 5, No 3: November 2016 Vol 5, No 2: July 2016 Vol 5, No 1: March 2016 Vol 4, No 3: November 2015 Vol 4, No 2: July 2015 Vol 4, No 1: March 2015 Vol 3, No 3: November 2014 Vol 3, No 2: July 2014 Vol 3, No 1: March 2014 Vol 2, No 3: November 2013 Vol 2, No 2: July 2013 Vol 2, No 1: March 2013 Vol 1, No 3: November 2012 Vol 1, No 2: July 2012 Vol 1, No 1: March 2012 More Issue