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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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A new fuzzy rule-based optimization approach for predicting the user behaviour classification in M-commerce Muniappan Ramaraj; Jothish Chembath; Balluru Thammaiahshetty Adishankar Nithya; Gnanakumar Ganesan; Balakrishnan Uma Shankari; Nagarajan Karthikeyan
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 12, No 3: November 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v12.i3.pp320-328

Abstract

A novel approach for classification of user behaviour prediction using proposed embracing the optimized fuzzy techniques to predicting the user data in M-commerce. Using this technique, network users can be monitored and their behavior categorized according to their activity. Unauthorized use of the website, network security breach attempts, firewalls, unauthorized access to the service and frequency of attempts. The proposed method has been adapted with the user classification to predict the predefine segregation of information to extract from user logs. Pattern recognition is a method for information discovery that results in current information patterns. Continuing items are a required task in various knowledge mining operations in pursuit of fascinating types from the data banks, including association rules, connections, sequences, episodes, classifications, bunches and much more. The functionality findings achieved in relation to precision and recall show that our technique can contribute to predicting more accurately than the different approaches. This paper focuses on to enhance the far better forecast for the mobile phone users through locating more reliable frequent patterns coming from the consumer deal data bank through looking at the body weight value of each thing collection and also examining the consumer activities on all time intervals.
Custom administering attention module for segmentation of magnetic resonance imaging of the brain Nagveni B. Sangolgi; Sasikala Sasikala
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 12, No 3: November 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v12.i3.pp376-383

Abstract

Taking into account how brain tumors and gliomas are notorious forms of cancer, the medical field has found several methods to diagnose these diseases, with many algorithms that can segment out the cancer cells in the magnetic resonance imaging (MRI) scans of the brain. This paper has proposed a similar segmenting algorithm called a custom administering attention module. This solution uses a custom U-Net model along with a custom administering attention module that uses an attention mechanism to classify and segment the glioma cells using long-range dependency of the feature maps. The customizations lead to a reduction in code complexity and memory cost. The final model has been tested on the BraTS 2019 dataset and has been compared with other state-of-the-art methods for displaying how much better the proposed model has performed in the category of enhancing, non-enhancing and peritumoral gliomas.
IoT-enabled system for monitoring and controlling vertical farming operations Harn Tung Ng; Zhi Kean Tham; Nurul Amani Abdul Rahim; Ammar Wafiq Rohim; Wei Wen Looi; Nur Syazreen Ahmad
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 12, No 3: November 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v12.i3.pp453-461

Abstract

In this paper, we present an internet of things (IoT) powered solution that facilitates effortless monitoring and management of vertical farming operations. Our proposed approach employs cost-effective embedded microcontrollers and sensors to keep a tab on crucial parameters like soil moisture, air humidity, and temperature. The data acquired from these sensors can be accessed through a web page that is compatible with all web browsers and smart gadgets such as mobile phones and tablets. Furthermore, the IoT platform offers users the ability to regulate soil moisture and administer ultraviolet light to plants. The system can bring many benefits such as enabling real-time monitoring and control of environmental conditions, reducing energy consumption, improving scalability and flexibility, and contributing to the sustainable and efficient production of food.
Balancing of four wire loads using linearized H-bridge static synchronous compensators Abdulkareem Mokif Obais; Ali Abdulkareem Mukheef
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 12, No 3: November 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v12.i3.pp462-477

Abstract

In this paper, a load balancing system is designed to balance the secondary phase currents of 11 kV/380 V, 50 Hz, 100 kVA power transformer in a three phase 4-wire, distribution network. The load balancing system is built of six identical modified static synchronous compensators (M-STATCOMs). Each M-STATCOM is constructed of a voltage source converter-based H-bridge controlled in capacitive and inductive modes as a linear compensating susceptance. The M-STATCOM current is controlled by varying its angle such that it exchanges pure reactive current with the utility grid. Three identical M-STATCOMs are connected in delta-form to balance the active phase currents of the power transformer, whereas the other three identical M-STATCOMs are connected in star-form to compensate for reactive currents. The M-STATCOMs in the delta-connected compensator are driven by 380 V line-to-line voltages, whilst, those connected in star-form are driven by 220 V phase voltages. The results of the 220 V and 380 V M-STATCOMs have exhibited linear and continuous control in capacitive and inductive regions of operation without steady-state harmonics. The proposed load balancing system has offered high flexibility during treating moderate and severe load unbalance conditions. It can involve any load unbalance within the power transformer current rating and even unbalance cases beyond the power transformer current rating.
Predicting yield of crop type and water requirement for a given plot of land using machine learning techniques Nitin Padriya; Nimisha Patel
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 12, No 3: November 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v12.i3.pp503-508

Abstract

Internet of things (IoT) smart technology enables new digital agriculture. Technology has become necessary to address today's challenges, and many sectors are automating their processes with the newest technologies. By maximizing fertiliser use to boost plant efficiency, smart agriculture, which is based on IoT technology, intends to assist producers and farmers in reducing waste while improving output. With IoT-based smart farming, farmers may better manage their animals, develop crops, save costs, and conserve resources. Climate monitoring, drought detection, agriculture and production, pollution distribution, and many more applications rely on the weather forecast. The accuracy of the forecast is determined by prior weather conditions across broad areas and over long periods. Machine learning algorithms can help us to build a model with proper accuracy. As a result, increasing the output on the limited acreage is important. IoT smart farming is a high-tech method that allows people to cultivate crops cleanly and sustainably. In agriculture, it is the use of current information and communication technologies.
A hybrid wrapper spider monkey optimization-simulated annealing model for optimal feature selection Bibhuprasad Sahu; Amrutanshu Panigrahi; Bibhu Dash; Pawan Kumar Sharma; Abhilash Pati
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 12, No 3: November 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v12.i3.pp360-375

Abstract

In this research, a hybrid wrapper model is proposed to identify the featured gene subset from the gene expression data. To balance the gap between exploration and exploitation, a hybrid model with a popular meta-heuristic algorithm named spider monkey optimizer (SMO) and simulated annealing (SA) is applied. In the proposed model, ReliefF is used as a filter to obtain the relevant gene subset from dataset by removing the noise and outliers prior to feeding the data to the wrapper SMO. To enhance the quality of the solution, simulated annealing is deployed as local search with the SMO in the second phase, which will guide to the detection of the most optimal feature subset. To evaluate the performance of the proposed model, support vector machine (SVM) as a fitness function to recognize the most informative biomarker gene from the cancer datasets along with University of California, Irvine (UCI) datasets. To further evaluate the model, 4 different classifiers (SVM, na¨ıve Bayes (NB), decision tree (DT), and k-nearest neighbors (KNN)) are used. From the experimental results and analysis, it’s noteworthy to accept that the ReliefF-SO-SA-SVM performs relatively better than its state-of-the-art counterparts. For cancer datasets, our model performs better in terms of accuracy with a maximum of 99.45%.
An internet of things-based touchless parking system using ESP32-CAM Vicky Andria Kusuma; Hamzah Arof; Sena Sukmananda Suprapto; Bambang Suharto; Rizky Amalia Sinulingga; Fadli Ama
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 12, No 3: November 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v12.i3.pp329-335

Abstract

As technology continues to advance, governments around the world have implemented health protocols to minimize direct contact between individuals and objects, in response to the ongoing COVID-19 outbreak. To address this need, a touchless parking portal was designed using a microcontroller-based and internet of things (IoT) -based system, with the Arduino UNO microcontroller device serving as the core component. The system employs an ultrasonic sensor HC-SR04 and passive infrared (PIR) to detect vehicles as they arrive at the portal area, in addition to requiring an ESP32-CAM camera, servo motor, light-emitting diode (LED), I2C 16x2 liquid crystal display (LCD), push button, universal serial bus (USB) to transistor-transistor logic (TTL) converter, power supply, and portal bar. The system builder software was developed using Arduino integrated development environment (IDE), Android, and Blynk. The authors conducted thorough testing and analysis of the system, concluding that its overall performance reaches 100%. Nevertheless, despite the extensive experimentation conducted, there remains a possibility that certain factors could still affect the results. Therefore, caution is advised when interpreting the outcomes of this experiment.
People identification via tongue print using fine-tuning deep learning Ahmed Shallal Obaid; Mohammed Y. Kamil; Basaad Hadi Hamza
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 12, No 3: November 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v12.i3.pp433-441

Abstract

Many person-verification systems are critical in security systems for verifying passage through doors opened to specific people using various techniques. People can use electronic payment methods and security apps to generate codes for quick, remote financial transactions. Older systems required precision and speed. Many alternative methods were developed by technology and artificial intelligence to make such operations simple and quick. The identification of tongue prints is discussed in this paper. Tongue prints, like fingerprints, are unique to each individual. The tongue was used in this study because it is unique among such organs. The tongue is protected by the lips. This guards against taking a tongue print by force. Some people distort their fingerprints, making fingerprint recognition systems unable to recognize them. Car accidents cause facial distortion, which distorts the system and prevents it from distinguishing facial prints, so the tongue was used as a fingerprint in this study. A database of 1,104 images for 138 Mustansiriyah University College of Science students yielded an average of eight images per individual. VGG16 was implemented for transfer learning and fine-tuning. In comparison to previous studies, the accuracy achieved was more than 91%.
Vehicle detection and classification using three variations of you only look once algorithm Gehad Saleh Ahmed Mohammed; Norizan Mat Diah; Zaidah Ibrahim; Nursuriati Jamil
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 12, No 3: November 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v12.i3.pp442-452

Abstract

Vehicle detection and classification are essential for advanced driver assistance systems (ADAS) and even traffic camera surveillance. Yet, it is challenging due to complex backgrounds, varying illumination intensities, occlusions, vehicle size, and type variations. This paper aims to apply you only look once (YOLO) since it has been proven to produce high object detection and classification accuracy. There are various versions of YOLO, and their performances differ. An investigation on the detection and classification performance of YOLOv3, YOLOv4, and YOLOv5 has been conducted. The training images were from common objects in context (COCO) and open image, two publicly available datasets. The testing input images were captured on a few highways in two main cities in Malaysia, namely Shah Alam and Kuala Lumpur. These images were captured using a mobile phone camera with different backgrounds during the day and night, representing different illuminations and varying types and sizes of vehicles. The accuracy and speed of detecting and classifying cars, trucks, buses, motorcycles, and bicycles have been evaluated. The experimental results show that YOLOv5 detects vehicles more accurately but slower than its predecessors, namely YOLOv4 and YOLOv3. Future work includes experimenting with newer versions of YOLO.
Smart surveillance using deep learning Amsaveni Avinashiappan; Harshavarthan Thiagarajan; Harshwarth Coimbatore Mahesh; Rohith Suresh
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 12, No 3: November 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v12.i3.pp423-432

Abstract

Smart surveillance systems play an important role in security today. The goal of security systems is to protect users against fires, car accidents, and other forms of violence. The primary function of these systems is to offer security in residential areas. In today’s culture, protecting our homes is critical. Surveillance, which ranges from private houses to large corporations, is critical in making us feel safe. There are numerous machine learning algorithms for home security systems; however, the deep learning convolutional neural network (CNN) technique outperforms the others. The Keras, Tensorflow, Cv2, Glob, Imutils, and PIL libraries are used to train and assess the detection method. A web application is used to provide a user-friendly environment. The flask web framework is used to construct it. The flash-mail, requests, and telegram application programming interface (API) apps are used in the alerting approach. The surveillance system tracks abnormal activities and uses machine learning to determine if the scenario is normal or not based on the acquired image. After capturing the image, it is compared with the existing dataset, and the model is trained using normal events. When there is an anomalous event, the model produces an output from which the mean distance for each frame is calculated.

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