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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 study of IoT based real-time monitoring of photovoltaic power plant Ouederni, Ramia; Bouaziz, Bechir; Bacha, Faouzi
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 14, No 1: March 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v14.i1.pp184-190

Abstract

Global electricity demand has increased in the last few years. This need is growing all the time as energy consumption increases using conventional energy, which will soon be phased out. So, we had to look at alternative energies, namely renewable energies. The largest and most efficient of these is solar energy, and to make the most of this energy with the greatest efficiency, the performance of these solar panels needs to be directly monitored. This study presents an independent monitoring system based on the internet of things (IoT) to measure essential factors (terminal voltage, load current, energy consumption, humidity, temperature, and light intensity). These values are realistic and accurate, based on the sensors used to measure the aforementioned factors and then using the Node MCU ESP8266 to transmit the analyzed data to the circuit. The Thingspeak platform was then employed to display, analyze, and store these results in real time.
Self-attention encoder-decoder with model adaptation for transliteration and translation tasks in regional language Nagaraja, Shanthala; Chandappa, Kiran Y.
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 14, No 1: March 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v14.i1.pp243-253

Abstract

The recent advancements in natural language processing (NLP) have highlighted the significance of integrating machine transliteration with translation for enhanced language services, particularly in the context of regional languages. This paper introduces a novel neural network architecture that leverages a self-attention mechanism to create an autoencoder without the need for iterative or convolutional processes. The selfattention mechanism operates on projection matrices, feature matrices, and target queries, utilizing the Softmax function for optimization. The introduction of the self-attention encoder-decoder with model adaptation (SAEDM) represents a breakthrough, marking a substantial enhancement in transliteration and translation accuracy over previous methodologies. This innovative approach employs both student and teacher models, with the student model's loss calculated through the probabilities and prediction labels via the negative log entropy function. The proposed architecture is distinctively designed at the character level, incorporating a word-to-word embedding framework, a beam search algorithm for sentence generation, and a binary classifier within the encoder-decoder structure to ensure the uniqueness of the content. The effectiveness of the proposed model is validated through comprehensive evaluations using transliteration and translation datasets in Kannada and Hindi languages, demonstrating its superior performance compared to existing models.
Development and evaluation of robotic exoskeleton arm for enhanced human load carrying efficiency Mohd Azam, Muhammad Aiman; Mohd Annuar, Khalil Azha; Mohamad Sapiee, Mohd Razali; Debnath, Sanjoy Kumar
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 14, No 1: March 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v14.i1.pp282-290

Abstract

In recent years, there has been a significant amount of research dedicated to the development of robotic exoskeleton systems. These technologies have been widely explored for their potential in virtual reality, human power enhancement, robotic rehabilitation, human power assist, and haptic interface applications. This research focuses on creating an exoskeleton arm that can assist individuals in carrying heavy objects. The exoskeleton arm is initially designed using Fusion 360, with the identification and calculation of important components such as the exoskeleton structure, motors serving as joints, an electromyography (EMG) sensor, and an Arduino UNO microcontroller. The research involves various aspects of mechanical design, electronic components, and programming. The effectiveness of the developed exoskeleton arm is then tested through experiments involving several individuals lifting a 2.5 kg and 5.0 kg load. The results of the experiments demonstrate that the force generated by the muscles is reduced when using the exoskeleton arm, compared to using a supporting system. Individuals' performance dropped by 36.06% to 50.44% when using an exoskeleton to lift 2.5 kg. This emphasises its effect on muscle activation and efficiency following physical activity. A 10.14% to 23.25% decline in a 5.0 kg lift shows nuanced impacts, emphasising the need for personalised modifications.
Design of medium grain integrated clock gater for low power clock network Nagaraja, Shylashree; Sathisha, Abhinav; Shivaraj, Mamatha Aruvanalli; Nanjundappa, Latha Bavikatte; Pandeshwara, Prakash Tunga
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 14, No 1: March 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v14.i1.pp117-125

Abstract

The very large scale integration (VLSI) applications were mainly dependent on area, reliability, and cost rather than power. The power-increasing demand was mainly due to the latest growth of electronic products such as portable mobile phones, laptops, and other devices that needs high speed and low power consumption. The power analysis provides insights on the switching activity of various sequential logic and thus would help early power optimization approaches to be incorporated in the design flow. The medium grain integrated clock gater insertion will help with synthesis flows for other low-power techniques to be applied. The power analysis is performed with a physically driven synthesis network for both leakage and dynamic. The power analysis revealed that medium grain clock gaters help with finer granularity of the clock gating principle thus improving gating efficiency. The medium grain clock gating techniques help the tool understand the activities of various sinks thus helping in the insertion of fine gaters as well. For a single medium grain clock gater, the power savings obtained were 41.37% and 79.35% without and with fine gater insertion respectively while cloning of the medium gaters resulted in 45.1% and 67.4% power savings without and with fine gater insertion respectively. The fine-grain integrated clock gating insertion incurred a maximum of 14.7% increased gate count.
TENS device for cervical pain during teleworking controlled remotely by mobile application Yauri, Ricardo; Balvin, Juan; Lobo, Renzo
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 14, No 1: March 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v14.i1.pp60-68

Abstract

Monitoring cervical muscle pain during teleworking, exacerbated by the COVID-19 pandemic and increased remote work, highlights electrotherapy as a crucial physical therapy tool to mitigate muscle pain and promote tissue recovery, addressing ergonomic and occupational health problems that affect the well-being of remote workers. The research proposes to design a transcutaneous electrical nerve stimulation (TENS) device to monitor cervical muscle pain during teleworking, addressing the urgent need for technological solutions to mitigate this problem and improve the quality of life of teleworkers through data acquisition and processing, hardware development, implementation device monitoring, and evaluation software. For this, a TENS device was designed with a graphical interface to treat muscle pain in the neck of teachers who do remote work, dividing it into four stages: signal acquisition and generation, Bluetooth communication with an Android device, signal conditioning, and amplification and protection, following a development scheme that includes circuit design in Proteus and the creation of a mobile application in App Inventor. In conclusion, it was obtained that the power supplies have an average error of less than 1%, indicating good general performance and confirming the consistency and optimal performance of the proposed therapies.
Waste incinerator monitoring system based on remote communication with android interface Sarosa, Moechammad; Wirayoga, Septriandi; Putri, Ratna Ika; Adhisuwignjo, Supriatna
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 14, No 1: March 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v14.i1.pp136-144

Abstract

Raya Ngijo Housing, one of the areas in Karangploso in Malang District has a temporary waste management team that organises the collection of waste from residents and sends it to the landfill. The process of collecting waste from residents is usually at the temporary disposal site (TPS) in the form of moving waste from residential cleaning vehicles and accommodated at the TPS until collection by the Malang District environmental service container for disposal to the transferred to landfills (TPA). Problems often occur when the container collection process is delayed for various reasons, so that the amount of rubbish in the TPS is excessive. One of the solutions made by the cleaning team is to burn excess waste and can be burned using a furnace. However, the combustion carried out cannot be ensured perfect combustion which is feared by the environmental service. Therefore, a remote communication-based furnace monitoring system and android application were made to ensure the perfection of the combustion process so that it could be monitored by the cleaning team. Parts per million (PPM) carbon dioxide (CO2) levels of combustion smoke and combustion temperature are also monitored and controlled in accordance with the safe standards set by the environmental agency.
Modeling of chimp optimization algorithm node localization scheme in wireless sensor networks Arunachalam, Sripriya; Vijaya Kumar, Ashok Kumar; Reddy, Desidi Narsimha; Pathipati, Harikrishna; Priyadarsini, Nethala Indira; Babu Ramisetti, Lova Naga
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 14, No 1: March 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v14.i1.pp221-230

Abstract

For smart environments in the digital age, wireless sensor networks (WSNs) are needed. Node localization (NL) in WSNs is complicated for recent researchers. WSN localization focuses on finding sensor nodes (SNs) in two dimensions. WSN NL provides decision-making information in packets sent to base stations. This article describes modeling of chimp optimization algorithm node localization system in wireless sensor networks (MCOANL-WSN). The MCOANL-WSN approach uses metaheuristic optimization to locate unknown network nodes. To simulate chimpanzees' cooperative hunting behavior, the MCOANL-WSN approach includes chimp optimization algorithm (COA) into the NL process. The system uses mathematical modeling to represent node collaboration to improve placements. COA-based localization is being proposed for dynamically responding to resource-constrained and dynamic WSNs. Wide-ranging simulations may assess the MCOANL-WSN system's scalability, energy efficiency, and localization accuracy. The findings demonstrate the superiority of the new modeling method over current NL schemes in improving WSN reliability and efficiency in various applications.
Central processing unit load reduction through application code optimization and memory management Bhadrayya, Sowmya Kandiga; Ravishankar, Vishwas Bangalore
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 14, No 1: March 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v14.i1.pp79-88

Abstract

Central processing unit (CPU) loading refers to the amount of processing power a CPU uses to execute a given set of commands or perform an exact task. Higher CPU load can lead to slower, sluggish performance, reduced lifespan, and reduced system stability. Using the CPU Load trace results, the performance bottlenecks can be identified and suitable methods can be adopted to reduce the load on the CPU. For an ideal embedded system, the CPU should be in idle state for around 70% of CPU usage time. In this paper, three types of optimization techniques are implemented, which include application code optimization, memory management, and implementing interrupt-driven data transfer. Application code can be optimized by getting rid of redundant code, duplicate functions and function inlining, function cloning which reduces the size of the code with increase in reusability. By moving the data, variables to data tightly coupled memory (DTCM) and instructions, functions to instruction tightly coupled memory (ITCM), the speed of the CPU increases which reduces the load on CPU. The conventional polling method which increases the CPU load can be reduced by implementing the same in interrupt-driven data transfer. The load on the CPU has reduced from 89.53% to 29.58%.
FPGA implementation of artificial neural network for PUF modeling Mispan, Mohd Syafiq; Ishak, Mohammad Haziq; Jidin, Aiman Zakwan; Mohd Nasir, Haslinah
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 14, No 1: March 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v14.i1.pp200-207

Abstract

Field-programmable gate array (FPGA) is a prominent device in developing the internet of things (IoT) application since it offers parallel computation, power efficiency, and scalability. The identification and authentication of these FPGAbased IoT applications are crucial to secure the user-sensitive data transmitted over IoT networks. Physical unclonable function (PUF) technology provides a great capability to be used as device identification and authentication for FPGAbased IoT applications. Nevertheless, conventional PUF-based authentication suffers a huge overhead in storing the challenge-response pairs (CRPs) in the verifier’s database. Therefore, in this paper, the FPGA implementation of the Arbiter-PUF model using an artificial neural network (ANN) is presented. The PUF model can generate the CRPs on-the-fly upon the authentication request (i.e., by a prover) to the verifier and eliminates huge storage of CRPs database in the verifier. The architecture of ANN (i.e., Arbiter-PUF model) is designed in Xilinx system generator and subsequently converted into intellectual property (IP). Further, the IP is programmed in Xilinx Artix-7 FPGA with other peripherals for CRPs generation and validation. The findings show that the Arbiter-PUF model implementation on FPGA using the ANN technique achieves approximately 98% accuracy. The model consumes 12,196 look-up tables (LUTs) and 67 mW power in FPGA.
Design of agrivoltaic system with internet of things control for chili fruit classification using the neural network method Wanayumini, Wanayumini; Satria, Habib; Rosnelly, Rika
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 14, No 1: March 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v14.i1.pp176-183

Abstract

Agriculture is a leading sector in the economy as well as the most dominant provider of employment for the Indonesian people. The fertile soil factor allows various types of fruit to be grown, including chilies. However, complex problems make chili farmers have limitations in implementing conventional farming systems. Therefore, the development of an agrivoltaic system with internet of things (IoT) integrated sensors on chili plants can help farmers more easily control, add vitamins, fertilizers, and provide plant nutrients that can be done automatically periodically based on a real-time clock schedule. This system also operates using photovoltaic (PV) as a pumping machine for water circulation. Other technologies such as mini smart cameras are also being developed to monitor and take pictures of chilies which will later be converted using the graphical user interface (GUI) application for segmentation. The method used in this chili fruit classification uses an artificial neural network in classifying ripe, raw, and rotten chilies. The classification results obtained an R value of 0.9, which means it is close to a value of 1 in the suitability of the chili image. Therefore, farmers will find it easier to sort the chilies that will be harvested.

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