IJRES Team - Academia.edu (original) (raw)
Papers by IJRES Team
International Journal of Reconfigurable and Embedded Systems (IJRES), 2025
The number of deaths has increased as a direct result of the increased frequency of traffic accid... more The number of deaths has increased as a direct result of the increased frequency of traffic accidents, congestion, and other risk factors. Developing countries have prioritised the development of intelligent transport systems in order to reduce pollution, traffic congestion, and wasted time. This article describes an intelligent transport system that leverages the internet of vehicles (IoV) and deep learning to forecast traffic congestion. Data is acquired using a car’s global positioning system (GPS), road and vehicle sensors, traffic cameras, and traffic speed, density, and flow. All acquired data is stored in one location on a cloud server. The cloud server also stores historical traffic, road, and vehicle data. Using particle swarm optimisation, features are improved. The optimised dataset is used to train and test recurrent neural networks (RNNs), support vector machines (SVMs), and multi layer perceptrons (MLPs). A deep learning algorithm can predict traffic congestion and make recommendations to drivers on how fast to travel and which route to take. The experimental effort employs the performance measurement system (PeMS) traffic dataset. RNN has achieved accuracy of 95.1%.
International Journal of Reconfigurable and Embedded Systems (IJRES), 2025
In recent years, there has been a significant amount of research dedicated to the development of ... more 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.
International Journal of Reconfigurable and Embedded Systems (IJRES), 2025
The existing system for computation completely incorporates Von-Neumann architecture which has li... more The existing system for computation completely incorporates Von-Neumann architecture which has limitations with respect to its memory, parallelism and power constraints. This has affected the efficiency of the computing system. Novel architectural solutions are required to meet the growing demands for improved computational efficiency and power management in very large scale integration (VLSI) systems. To deal with the large-scale data, computation in memory (CIM) has been introduced. The paper presents the half subtractor circuit and the In-memory computation co-design using eight transistors static random access memory (SRAM) cell whose read circuitry is transmission gate based. The proposed half-subtractor with the CIM is implementation is carried out in 180 nm complementary metal– oxide–semiconductor (CMOS) technology. The sensing scheme used is the latch-based sense amplifier along with the 8T SRAM cell. The proposed SRAM with transmission-gate based read circuitry along with latch-based sense amplifier reduces the delay and power consumed during the read operation significantly and a bit reduction during the write operation. The static noise margin (SNM) for read operation has been increased by 9% in the transmission gate-based SRAM as compared to conventional 8T SRAM. The delay of the proposed design has been reduced by 53% during the read operation and 4.43% during the write operation. The power consumed has been reduced by 3% and 8.6% during read and write operations, respectively.
International Journal of Reconfigurable and Embedded Systems (IJRES), 2025
This study focuses on the development of electrical power forecasting based on electricity usage ... more This study focuses on the development of electrical power forecasting based on electricity usage in Wuzhou, China. To develop a forecasting model, the important features need to be identified. Therefore, this study investigates the performance of the feature selection method, focusing on the mutual information as a filter and random forest as a wrapper-based feature selection. From the experiment, six features have been chosen, whereby both feature selection methods chose almost identical features. Later, the selected features are trained and tested with common machine learning models, namely random forest regressor, support vector regression (SVR), k-nearest neighbor (KNN) regressor, and extreme gradient boosting (XGBoost) regressor. The performances of the feature selections tested on each of the models are measured in terms of mean absolute error (MAE), root mean square error (RMSE) and coefficient of determination (R²). Findings from the experiment revealed that XGBoost outperform the other machine learning models with RMSE 0.9566 and R² indicated with 0.2561. However, SVR outperformed XGBoost and other model by obtaining MAE 0.6028. It can be concluded that the performance of filter-based outperformed the embedded feature selection.
International Journal of Reconfigurable and Embedded Systems (IJRES), 2025
Emotion detection is essential in many domains including affective computing, psychological asses... more Emotion detection is essential in many domains including affective computing, psychological assessment, and human computer interaction (HCI). It contrasts the study of emotion detection across text, image, and speech modalities to evaluate state-of-the-art approaches in each area and identify their benefits and shortcomings. We looked at present methods, datasets, and evaluation criteria by conducting a comprehensive literature review. In order to conduct our study, we collect data, clean it up, identify its characteristics and then use deep learning (DL) models. In our experiments we performed text-based emotion identification using long short-term memory (LSTM), term frequency-inverse document frequency (TF-IDF) vectorizer, and image-based emotion recognition using a convolutional neural network (CNN) algorithm. Contributing to the body of knowledge in emotion recognition, our study's results provide light on the inner workings of different modalities. Experimental findings validate the efficacy of the proposed method while also highlighting areas for improvement.
International Journal of Reconfigurable and Embedded Systems (IJRES), 2025
The recent advancements in natural language processing (NLP) have highlighted the significance of... more 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.
International Journal of Reconfigurable and Embedded Systems (IJRES), 2025
In the current furniture industry, making furniture goes through many steps. There are ordering m... more In the current furniture industry, making furniture goes through many steps. There are ordering materials, designing, building a prototype, and testing samples. This process is considered quite complex, requiring significant costs, and lengthy production time. The application of finite element analysis (FEA) can be a solution to simulate the furniture manufacturing process. Objective of this research was to determine FEA could substitute making and test prototype furniture thereby saving costs and time. This method utilizes ANSYS 18.1 software for more accurate and rapid calculations, incorporating load variables of 400 N, 600 N, 800 N, and 1,000 N, along with gravitational acceleration of 10 \frac{m}{s^2}. The research evaluates the difference (expressed as a percentage) between the results obtained from simulations and those obtained directly from experiments, considering maximum equivalent stress, maximum principal stress, and total deformation values. The final step involves comparing the simulation with direct testing in terms of cost and time. The research results show an average error factor of 5% across all aspect. In terms of cost, the method can save 1,807 USD and reduce production time by up to one month. From these findings, it can be concluded that the process of prototyping and sample testing can be replaced using the finite element method.
International Journal of Reconfigurable and Embedded Systems (IJRES), 2025
For smart environments in the digital age, wireless sensor networks (WSNs) are needed. Node local... more 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.
International Journal of Reconfigurable and Embedded Systems (IJRES), 2025
Ultra-wide band (UWB) is a wireless technology deployed for transmitting data at high rates over ... more Ultra-wide band (UWB) is a wireless technology deployed for transmitting data at high rates over short distances. Similar to Wi-Fi and Bluetooth, UWB is a radio frequency (RF) technology that operates via radio waves. To remove minor noise and glitches, low noise amplifier (LNA) is required because it amplifies weak signals without significantly adding noise. However, UWB has multiple frequencies that require coefficient change due to frequency variations. When low-pass filter (LPF) is employed to solve this, updates are necessary to manage delay and power because the LPF feedback is connected to LNA to increase delay and power consumption. In this research, LNA with a pre-distortion architecture is proposed to remove minor noises and small glitches. It is processed by using pre-distortion as an active component which reduces delay and power consumption in UWB. The pre-distortion process operates in the subthreshold voltage range by providing a transistor to each frequency as input, inturn effectively reducing the noise. The proposed LNA with pre-distortion architecture is developed on 180-nm complementary metal-oxide semiconductor (CMOS) technology using Cadense ASIC tool. The proposed architecture achieves a noise figure (NF) of 2.16 dB and less power consumption of 43.06×10-6 W when compared to the existing techniques namely, cascade amplifiers, W-band LNA, reflectionless receiver (RX), and broadband RF receiver front-end circuits.
International Journal of Reconfigurable and Embedded Systems (IJRES), 2025
Field-programmable gate array (FPGA) is a prominent device in developing the internet of things (... more 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.
International Journal of Reconfigurable and Embedded Systems (IJRES), 2025
Citizen insecurity in underdeveloped third world countries is aggravated by poor management of ar... more Citizen insecurity in underdeveloped third world countries is aggravated by poor management of arms control and illegal trafficking, which requires information technology solutions in intelligent video surveillance systems for the detection of lethal weapons. The literature review highlights the need for an intelligent video surveillance system to combat high crime, using fog computing, which processes data in real time for the detection of weapons and other crimes. Furthermore, at an international level, solutions based on artificial intelligence and deep learning are being implemented for object recognition and weapons detection. Therefore, this paper describes the design of an intelligent video surveillance system based on artificial vision, fog and edge computing to detect lethal weapons in domestic environments, performing weapon classification and data transmission to police centers. The intelligent video surveillance system allows detecting lethal weapons and operates in three stages: an edge node with a Raspberry Pi 4; a detection algorithm based on a convolutional neural network with YOLOv5; and streaming tagged images to a security unit via WhatsApp. The main conclusion is that the system achieved a precision greater than 0.85 and a quick and efficient response in sending alerts, representing a scalable and effective solution against home burglary.
International Journal of Reconfigurable and Embedded Systems (IJRES), 2025
Global electricity demand has increased in the last few years. This need is growing all the time ... more 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.
International Journal of Reconfigurable and Embedded Systems (IJRES), 2025
Agriculture is a leading sector in the economy as well as the most dominant provider of employmen... more 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.
International Journal of Reconfigurable and Embedded Systems (IJRES), 2025
This paper compares the performance of various wireless technologies: ZigBee, long range (LoRa), ... more This paper compares the performance of various wireless technologies: ZigBee, long range (LoRa), and narrowband internet of things (NB-IoT), which support smart building applications. The highlight of this work is that we focus on wireless communication between the floors of the building by analyzing the performance metrics using the received signal strength indicator (RSSI) and packet loss ratio (PLR). First, the ZigBee tests confirmed reliable packet delivery without any loss over distances up to 40 meters on the same floor, with RSSI results ranging from -65.5 to -87.5 dBm. ZigBee also maintained signal transmission through one cross-floor level, with RSSI values between -60 and -119 dBm. The second set of tests, with LoRa, indicated signal transmission over several floors with slightly improved RSSI values for the 2 dBi antenna compared to those for the -4 dBi antenna, despite increased packet loss with distance. Finally, NB-IoT showed the most consistent long-range connectivity, achieving a stable signal up to 458 meters from the base station with RSSI levels varying from -55.6 to -74.6 dBm, without packet loss in all tests. This study demonstrates how such technologies could be used in smart buildings and provides suggestions on how to determine the most suitable systems and configure them to ensure reliable communication networks within the building.
International Journal of Reconfigurable and Embedded Systems (IJRES), 2025
Electricity is a crucial aspect in human life. With population growth, ongoing regional developme... more Electricity is a crucial aspect in human life. With population growth, ongoing regional development, and continuous construction activities, the demand for electricity and fuel in Indonesia is increasing. The substantial power consumption leads to larger financial expenditures for the community. Additionally, the use of electricity, as it has been traditionally employed, has negative environmental impacts. Solutions are needed to address these issues, and one effort involves the use of renewable energy, such as the development of solar power plants (PLTS). PLTS, also known as solar cells, is preferred as it can be used for various relevant purposes in different locations, particularly in offices, factories, residential areas, and others. However, the use of static, single-axis, and dual-axis solar panels still has drawbacks, such as suboptimal sunlight intensity and high motor power consumption. Therefore, a flexible-axis solar panel tracking system has been developed to follow the direction of sunlight, ensuring optimal power efficiency, and significant electricity generation. The flexible-axis tracker system results in a 34.13% increase in power efficiency.
This paper presents an innovative smart key system designed to enhance the safety and convenience... more This paper presents an innovative smart key system designed to enhance the safety and convenience of hotel guests. The system employs an iterative, agile approach encompassing the phases of requirement analysis, design, implementation, and testing. Key components of the input circuitry include limit switches, RFID-RC522 and SW420 vibration sensors, which collectively gather data. This data is processed using an Arduino Uno microcontroller and integrated with internet of things (IoT) technology. On the output side, the system incorporates a solenoid lock and is capable of promptly notifying users via Telegram in response to unauthorized access attempts. Importantly, the system can distinguish between vibrations caused by unauthorized entry and those from legitimate usage. Rigorous testing validates its efficacy, issuing Telegram alerts promptly when detecting security breaches. This technological advancement significantly enhances hotel room security, providing an intelligent real-time solution. The fusion of IoT, Arduino microcontroller, and precise sensor configuration underscores the system's reliability, setting new benchmarks for security in the hospitality sector. The comprehensive approach detailed in this paper offers valuable insights applicable to a wide range of security applications.
International Journal of Reconfigurable and Embedded Systems (IJRES), 2025
Raya Ngijo Housing, one of the areas in Karangploso in Malang District has a temporary waste mana... more 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.
International Journal of Reconfigurable and Embedded Systems (IJRES), 2025
Natural disasters such as floods can cause many losses to humans, such as material losses, trauma... more Natural disasters such as floods can cause many losses to humans, such as material losses, trauma for the victims, and loss of life. Floods that occur can be caused by various factors such as human activity itself which results in changes in natural spatial planning, so the arrival of floods is also difficult to detect with certainty. Based on this, it is necessary to develop a technological innovation that helps provide a warning of the arrival of a natural disaster. The ESP32 microcontroller is one of the technologies that can be used to create an early warning system for the arrival of floods. The design and manufacture of this technology certainly involves modeling, algorithm planning, assembly of the components of the tools used, including wiring and mechanics as needed. This tool uses an internet of things (IoT) system with the help of an ESP32 microcontroller that supports integration via Wi-Fi and Bluetooth so that it can be connected to a smartphone device as a notification receiver in real time and accurately by notifying the water level which will be an indicator of potential flooding, so that people are more alert in the face of flooding to prevent and minimize the losses that will be experienced.
International Journal of Reconfigurable and Embedded Systems (IJRES), 2025
The very large scale integration (VLSI) applications were mainly dependent on area, reliability, ... more 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.
International Journal of Reconfigurable and Embedded Systems (IJRES), 2025
In many digital systems like high-performance computing and digital signal processing, parallel p... more In many digital systems like high-performance computing and digital signal processing, parallel prefix adders are vital. Field programmable gate array (FPGA) technology is a well-known platform for developing parallel prefix adders. FPGA performance depends on bit size of the adder, the adder structure chosen, and the implementation specifications. An examination of the performance and area of parallel prefix adders developed using FPGA technology is presented in this research work. We look into how different design factors, such as the adder structure and the number of input bits, affect the performance and area of parallel prefix adders. The different adders used are Sklansky, Kogge-Stone, Brent-Kung, Han-Carlson, and Ladner-Fisher adders. These adders are implemented using Verilog hardware description language (Verilog HDL) on FPGA boards. The performance is significantly influenced by choice of adder structure and design factors optimized for area or performance. The suggestions for choosing the best adder structure and design factors for the best performance or optimized area are obtained from the synthesis results. Ladner-Fisher adders is best parallel prefix adder with respect area and performance compared with the Sklansky, Kogge-Stone, Brent-Kung and Han-Carlson. Our synthesis can be used as a guide for designers looking to construct specific hardware on FPGA.
International Journal of Reconfigurable and Embedded Systems (IJRES), 2025
The number of deaths has increased as a direct result of the increased frequency of traffic accid... more The number of deaths has increased as a direct result of the increased frequency of traffic accidents, congestion, and other risk factors. Developing countries have prioritised the development of intelligent transport systems in order to reduce pollution, traffic congestion, and wasted time. This article describes an intelligent transport system that leverages the internet of vehicles (IoV) and deep learning to forecast traffic congestion. Data is acquired using a car’s global positioning system (GPS), road and vehicle sensors, traffic cameras, and traffic speed, density, and flow. All acquired data is stored in one location on a cloud server. The cloud server also stores historical traffic, road, and vehicle data. Using particle swarm optimisation, features are improved. The optimised dataset is used to train and test recurrent neural networks (RNNs), support vector machines (SVMs), and multi layer perceptrons (MLPs). A deep learning algorithm can predict traffic congestion and make recommendations to drivers on how fast to travel and which route to take. The experimental effort employs the performance measurement system (PeMS) traffic dataset. RNN has achieved accuracy of 95.1%.
International Journal of Reconfigurable and Embedded Systems (IJRES), 2025
In recent years, there has been a significant amount of research dedicated to the development of ... more 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.
International Journal of Reconfigurable and Embedded Systems (IJRES), 2025
The existing system for computation completely incorporates Von-Neumann architecture which has li... more The existing system for computation completely incorporates Von-Neumann architecture which has limitations with respect to its memory, parallelism and power constraints. This has affected the efficiency of the computing system. Novel architectural solutions are required to meet the growing demands for improved computational efficiency and power management in very large scale integration (VLSI) systems. To deal with the large-scale data, computation in memory (CIM) has been introduced. The paper presents the half subtractor circuit and the In-memory computation co-design using eight transistors static random access memory (SRAM) cell whose read circuitry is transmission gate based. The proposed half-subtractor with the CIM is implementation is carried out in 180 nm complementary metal– oxide–semiconductor (CMOS) technology. The sensing scheme used is the latch-based sense amplifier along with the 8T SRAM cell. The proposed SRAM with transmission-gate based read circuitry along with latch-based sense amplifier reduces the delay and power consumed during the read operation significantly and a bit reduction during the write operation. The static noise margin (SNM) for read operation has been increased by 9% in the transmission gate-based SRAM as compared to conventional 8T SRAM. The delay of the proposed design has been reduced by 53% during the read operation and 4.43% during the write operation. The power consumed has been reduced by 3% and 8.6% during read and write operations, respectively.
International Journal of Reconfigurable and Embedded Systems (IJRES), 2025
This study focuses on the development of electrical power forecasting based on electricity usage ... more This study focuses on the development of electrical power forecasting based on electricity usage in Wuzhou, China. To develop a forecasting model, the important features need to be identified. Therefore, this study investigates the performance of the feature selection method, focusing on the mutual information as a filter and random forest as a wrapper-based feature selection. From the experiment, six features have been chosen, whereby both feature selection methods chose almost identical features. Later, the selected features are trained and tested with common machine learning models, namely random forest regressor, support vector regression (SVR), k-nearest neighbor (KNN) regressor, and extreme gradient boosting (XGBoost) regressor. The performances of the feature selections tested on each of the models are measured in terms of mean absolute error (MAE), root mean square error (RMSE) and coefficient of determination (R²). Findings from the experiment revealed that XGBoost outperform the other machine learning models with RMSE 0.9566 and R² indicated with 0.2561. However, SVR outperformed XGBoost and other model by obtaining MAE 0.6028. It can be concluded that the performance of filter-based outperformed the embedded feature selection.
International Journal of Reconfigurable and Embedded Systems (IJRES), 2025
Emotion detection is essential in many domains including affective computing, psychological asses... more Emotion detection is essential in many domains including affective computing, psychological assessment, and human computer interaction (HCI). It contrasts the study of emotion detection across text, image, and speech modalities to evaluate state-of-the-art approaches in each area and identify their benefits and shortcomings. We looked at present methods, datasets, and evaluation criteria by conducting a comprehensive literature review. In order to conduct our study, we collect data, clean it up, identify its characteristics and then use deep learning (DL) models. In our experiments we performed text-based emotion identification using long short-term memory (LSTM), term frequency-inverse document frequency (TF-IDF) vectorizer, and image-based emotion recognition using a convolutional neural network (CNN) algorithm. Contributing to the body of knowledge in emotion recognition, our study's results provide light on the inner workings of different modalities. Experimental findings validate the efficacy of the proposed method while also highlighting areas for improvement.
International Journal of Reconfigurable and Embedded Systems (IJRES), 2025
The recent advancements in natural language processing (NLP) have highlighted the significance of... more 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.
International Journal of Reconfigurable and Embedded Systems (IJRES), 2025
In the current furniture industry, making furniture goes through many steps. There are ordering m... more In the current furniture industry, making furniture goes through many steps. There are ordering materials, designing, building a prototype, and testing samples. This process is considered quite complex, requiring significant costs, and lengthy production time. The application of finite element analysis (FEA) can be a solution to simulate the furniture manufacturing process. Objective of this research was to determine FEA could substitute making and test prototype furniture thereby saving costs and time. This method utilizes ANSYS 18.1 software for more accurate and rapid calculations, incorporating load variables of 400 N, 600 N, 800 N, and 1,000 N, along with gravitational acceleration of 10 \frac{m}{s^2}. The research evaluates the difference (expressed as a percentage) between the results obtained from simulations and those obtained directly from experiments, considering maximum equivalent stress, maximum principal stress, and total deformation values. The final step involves comparing the simulation with direct testing in terms of cost and time. The research results show an average error factor of 5% across all aspect. In terms of cost, the method can save 1,807 USD and reduce production time by up to one month. From these findings, it can be concluded that the process of prototyping and sample testing can be replaced using the finite element method.
International Journal of Reconfigurable and Embedded Systems (IJRES), 2025
For smart environments in the digital age, wireless sensor networks (WSNs) are needed. Node local... more 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.
International Journal of Reconfigurable and Embedded Systems (IJRES), 2025
Ultra-wide band (UWB) is a wireless technology deployed for transmitting data at high rates over ... more Ultra-wide band (UWB) is a wireless technology deployed for transmitting data at high rates over short distances. Similar to Wi-Fi and Bluetooth, UWB is a radio frequency (RF) technology that operates via radio waves. To remove minor noise and glitches, low noise amplifier (LNA) is required because it amplifies weak signals without significantly adding noise. However, UWB has multiple frequencies that require coefficient change due to frequency variations. When low-pass filter (LPF) is employed to solve this, updates are necessary to manage delay and power because the LPF feedback is connected to LNA to increase delay and power consumption. In this research, LNA with a pre-distortion architecture is proposed to remove minor noises and small glitches. It is processed by using pre-distortion as an active component which reduces delay and power consumption in UWB. The pre-distortion process operates in the subthreshold voltage range by providing a transistor to each frequency as input, inturn effectively reducing the noise. The proposed LNA with pre-distortion architecture is developed on 180-nm complementary metal-oxide semiconductor (CMOS) technology using Cadense ASIC tool. The proposed architecture achieves a noise figure (NF) of 2.16 dB and less power consumption of 43.06×10-6 W when compared to the existing techniques namely, cascade amplifiers, W-band LNA, reflectionless receiver (RX), and broadband RF receiver front-end circuits.
International Journal of Reconfigurable and Embedded Systems (IJRES), 2025
Field-programmable gate array (FPGA) is a prominent device in developing the internet of things (... more 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.
International Journal of Reconfigurable and Embedded Systems (IJRES), 2025
Citizen insecurity in underdeveloped third world countries is aggravated by poor management of ar... more Citizen insecurity in underdeveloped third world countries is aggravated by poor management of arms control and illegal trafficking, which requires information technology solutions in intelligent video surveillance systems for the detection of lethal weapons. The literature review highlights the need for an intelligent video surveillance system to combat high crime, using fog computing, which processes data in real time for the detection of weapons and other crimes. Furthermore, at an international level, solutions based on artificial intelligence and deep learning are being implemented for object recognition and weapons detection. Therefore, this paper describes the design of an intelligent video surveillance system based on artificial vision, fog and edge computing to detect lethal weapons in domestic environments, performing weapon classification and data transmission to police centers. The intelligent video surveillance system allows detecting lethal weapons and operates in three stages: an edge node with a Raspberry Pi 4; a detection algorithm based on a convolutional neural network with YOLOv5; and streaming tagged images to a security unit via WhatsApp. The main conclusion is that the system achieved a precision greater than 0.85 and a quick and efficient response in sending alerts, representing a scalable and effective solution against home burglary.
International Journal of Reconfigurable and Embedded Systems (IJRES), 2025
Global electricity demand has increased in the last few years. This need is growing all the time ... more 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.
International Journal of Reconfigurable and Embedded Systems (IJRES), 2025
Agriculture is a leading sector in the economy as well as the most dominant provider of employmen... more 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.
International Journal of Reconfigurable and Embedded Systems (IJRES), 2025
This paper compares the performance of various wireless technologies: ZigBee, long range (LoRa), ... more This paper compares the performance of various wireless technologies: ZigBee, long range (LoRa), and narrowband internet of things (NB-IoT), which support smart building applications. The highlight of this work is that we focus on wireless communication between the floors of the building by analyzing the performance metrics using the received signal strength indicator (RSSI) and packet loss ratio (PLR). First, the ZigBee tests confirmed reliable packet delivery without any loss over distances up to 40 meters on the same floor, with RSSI results ranging from -65.5 to -87.5 dBm. ZigBee also maintained signal transmission through one cross-floor level, with RSSI values between -60 and -119 dBm. The second set of tests, with LoRa, indicated signal transmission over several floors with slightly improved RSSI values for the 2 dBi antenna compared to those for the -4 dBi antenna, despite increased packet loss with distance. Finally, NB-IoT showed the most consistent long-range connectivity, achieving a stable signal up to 458 meters from the base station with RSSI levels varying from -55.6 to -74.6 dBm, without packet loss in all tests. This study demonstrates how such technologies could be used in smart buildings and provides suggestions on how to determine the most suitable systems and configure them to ensure reliable communication networks within the building.
International Journal of Reconfigurable and Embedded Systems (IJRES), 2025
Electricity is a crucial aspect in human life. With population growth, ongoing regional developme... more Electricity is a crucial aspect in human life. With population growth, ongoing regional development, and continuous construction activities, the demand for electricity and fuel in Indonesia is increasing. The substantial power consumption leads to larger financial expenditures for the community. Additionally, the use of electricity, as it has been traditionally employed, has negative environmental impacts. Solutions are needed to address these issues, and one effort involves the use of renewable energy, such as the development of solar power plants (PLTS). PLTS, also known as solar cells, is preferred as it can be used for various relevant purposes in different locations, particularly in offices, factories, residential areas, and others. However, the use of static, single-axis, and dual-axis solar panels still has drawbacks, such as suboptimal sunlight intensity and high motor power consumption. Therefore, a flexible-axis solar panel tracking system has been developed to follow the direction of sunlight, ensuring optimal power efficiency, and significant electricity generation. The flexible-axis tracker system results in a 34.13% increase in power efficiency.
This paper presents an innovative smart key system designed to enhance the safety and convenience... more This paper presents an innovative smart key system designed to enhance the safety and convenience of hotel guests. The system employs an iterative, agile approach encompassing the phases of requirement analysis, design, implementation, and testing. Key components of the input circuitry include limit switches, RFID-RC522 and SW420 vibration sensors, which collectively gather data. This data is processed using an Arduino Uno microcontroller and integrated with internet of things (IoT) technology. On the output side, the system incorporates a solenoid lock and is capable of promptly notifying users via Telegram in response to unauthorized access attempts. Importantly, the system can distinguish between vibrations caused by unauthorized entry and those from legitimate usage. Rigorous testing validates its efficacy, issuing Telegram alerts promptly when detecting security breaches. This technological advancement significantly enhances hotel room security, providing an intelligent real-time solution. The fusion of IoT, Arduino microcontroller, and precise sensor configuration underscores the system's reliability, setting new benchmarks for security in the hospitality sector. The comprehensive approach detailed in this paper offers valuable insights applicable to a wide range of security applications.
International Journal of Reconfigurable and Embedded Systems (IJRES), 2025
Raya Ngijo Housing, one of the areas in Karangploso in Malang District has a temporary waste mana... more 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.
International Journal of Reconfigurable and Embedded Systems (IJRES), 2025
Natural disasters such as floods can cause many losses to humans, such as material losses, trauma... more Natural disasters such as floods can cause many losses to humans, such as material losses, trauma for the victims, and loss of life. Floods that occur can be caused by various factors such as human activity itself which results in changes in natural spatial planning, so the arrival of floods is also difficult to detect with certainty. Based on this, it is necessary to develop a technological innovation that helps provide a warning of the arrival of a natural disaster. The ESP32 microcontroller is one of the technologies that can be used to create an early warning system for the arrival of floods. The design and manufacture of this technology certainly involves modeling, algorithm planning, assembly of the components of the tools used, including wiring and mechanics as needed. This tool uses an internet of things (IoT) system with the help of an ESP32 microcontroller that supports integration via Wi-Fi and Bluetooth so that it can be connected to a smartphone device as a notification receiver in real time and accurately by notifying the water level which will be an indicator of potential flooding, so that people are more alert in the face of flooding to prevent and minimize the losses that will be experienced.
International Journal of Reconfigurable and Embedded Systems (IJRES), 2025
The very large scale integration (VLSI) applications were mainly dependent on area, reliability, ... more 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.
International Journal of Reconfigurable and Embedded Systems (IJRES), 2025
In many digital systems like high-performance computing and digital signal processing, parallel p... more In many digital systems like high-performance computing and digital signal processing, parallel prefix adders are vital. Field programmable gate array (FPGA) technology is a well-known platform for developing parallel prefix adders. FPGA performance depends on bit size of the adder, the adder structure chosen, and the implementation specifications. An examination of the performance and area of parallel prefix adders developed using FPGA technology is presented in this research work. We look into how different design factors, such as the adder structure and the number of input bits, affect the performance and area of parallel prefix adders. The different adders used are Sklansky, Kogge-Stone, Brent-Kung, Han-Carlson, and Ladner-Fisher adders. These adders are implemented using Verilog hardware description language (Verilog HDL) on FPGA boards. The performance is significantly influenced by choice of adder structure and design factors optimized for area or performance. The suggestions for choosing the best adder structure and design factors for the best performance or optimized area are obtained from the synthesis results. Ladner-Fisher adders is best parallel prefix adder with respect area and performance compared with the Sklansky, Kogge-Stone, Brent-Kung and Han-Carlson. Our synthesis can be used as a guide for designers looking to construct specific hardware on FPGA.