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25,002 Article Results

Design of agrivoltaic system with internet of things control for chili fruit classification using the neural network method

10.11591/ijres.v14.i1.pp176-183
Wanayumini Wanayumini , Habib Satria , Rika Rosnelly
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.
Volume: 14
Issue: 1
Page: 176-183
Publish at: 2025-03-01

An internet of things-driven smart key system with real-time alerts: innovations in hotel security

10.11591/ijres.v14.i1.pp145-156
Putra Jaya , Ryan Fikri , Agariadne Dwinggo Samala , Dimas Sanjaya
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.
Volume: 14
Issue: 1
Page: 145-156
Publish at: 2025-03-01

Video surveillance system based on artificial vision and fog computing for the detection of lethal weapons

10.11591/ijres.v14.i1.pp191-199
Ricardo Yauri , José Monterrey
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.
Volume: 14
Issue: 1
Page: 191-199
Publish at: 2025-03-01

Modeling of chimp optimization algorithm node localization scheme in wireless sensor networks

10.11591/ijres.v14.i1.pp221-230
Sripriya Arunachalam , Ashok Kumar Vijaya Kumar , Desidi Narsimha Reddy , Harikrishna Pathipati , Nethala Indira Priyadarsini , Lova Naga Babu Ramisetti
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.
Volume: 14
Issue: 1
Page: 221-230
Publish at: 2025-03-01

Central processing unit load reduction through application code optimization and memory management

10.11591/ijres.v14.i1.pp79-88
Sowmya Kandiga Bhadrayya , Vishwas Bangalore Ravishankar
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%.
Volume: 14
Issue: 1
Page: 79-88
Publish at: 2025-03-01

Waste incinerator monitoring system based on remote communication with android interface

10.11591/ijres.v14.i1.pp136-144
Moechammad Sarosa , Septriandi Wirayoga , Ratna Ika Putri , Supriatna Adhisuwignjo
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
Volume: 14
Issue: 1
Page: 136-144
Publish at: 2025-03-01

FPGA implementation of artificial neural network for PUF modeling

10.11591/ijres.v14.i1.pp200-207
Mohd Syafiq Mispan , Mohammad Haziq Ishak , Aiman Zakwan Jidin , Haslinah Mohd Nasir
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.
Volume: 14
Issue: 1
Page: 200-207
Publish at: 2025-03-01

Analysing feature selection: impacts towards forecasting electricity power consumption

10.11591/ijres.v14.i1.pp265-272
Azman Ab Malik , Lyu Tao , Noormadinah Allias , Irni Hamiza Hamzah
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.
Volume: 14
Issue: 1
Page: 265-272
Publish at: 2025-03-01

TENS device for cervical pain during teleworking controlled remotely by mobile application

10.11591/ijres.v14.i1.pp60-68
Ricardo Yauri , Juan Balvin , Renzo Lobo
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.
Volume: 14
Issue: 1
Page: 60-68
Publish at: 2025-03-01

A study of IoT based real-time monitoring of photovoltaic power plant

10.11591/ijres.v14.i1.pp184-190
Ramia Ouederni , Bechir Bouaziz , Faouzi Bacha
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.
Volume: 14
Issue: 1
Page: 184-190
Publish at: 2025-03-01

Self-attention encoder-decoder with model adaptation for transliteration and translation tasks in regional language

10.11591/ijres.v14.i1.pp243-253
Shanthala Nagaraja , Kiran Y. Chandappa
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.
Volume: 14
Issue: 1
Page: 243-253
Publish at: 2025-03-01

Organic solar cells: a study on material selection and fabrication precision

10.11591/ijape.v14.i1.pp138-145
Karthika Krishnakumar , Ashish Grover , Pardeep Kumar , Asit Patra
The accelerating development of renewable energy technologies is imperative for addressing the problems of climate change and resource depletion. Solar energy, ideal for distributed power generation and more environmentally friendly, is integral to the progression of solar technology. Organic solar cells (OSCs) have become a key innovation in this domain, offering a promising alternative to traditional solar technologies. OSCs have received a lot of interest in the preceding years owing to their capacity to increase efficiency, affordability, and longevity. However, a dearth of research and development activities aimed at improving organic photovoltaic systems exists. This work details the laborious process of building a Bulk heterojunction (BHJ) OSC, describing the manufacturing stages and subsequent device characterization. OSCs were created in this work using three active layer materials: P3HT:PCBM, PTB7:PCBM, and PCDTBT:PCBM. The comparative analysis revealed significant efficiency disparities, with PCDTBT:PCBM exhibiting superior performance and electrical properties, while challenges were encountered with aged materials, emphasizing the relevance of meticulous material handling and the use of cutting-edge fabrication machinery in achieving efficient solar cell production.
Volume: 14
Issue: 1
Page: 138-145
Publish at: 2025-03-01

Optimizing microgrid designs towards net-zero emissions for smart cities: addressing energy disparities and access issues in Northern and North-eastern India

10.11591/ijape.v14.i1.pp127-137
Albert Paul Arunkumar , Selvakumar Kuppusamy
Providing affordable and clean energy is a significant sub-sector of the Smart Cities Mission proposed by India. This research investigates the development of optimal microgrid designs for smart cities in northern and north-eastern India to address regional energy disparities and access issues. In the northern zone, characterized by uneven urban-rural infrastructure and high-power demand, microgrids offer localized, reliable solutions that reduce dependency on centralized systems and enhance energy efficiency. In the north-eastern zone, where geographical isolation and underdeveloped infrastructure hinder energy access, microgrids provide decentralized power generation and distribution, improving access in remote areas. The proposed microgrid designs aim to enhance energy reliability, efficiency, and accessibility by integrating renewable energy sources. The proposed system is analyzed for technical and economic feasibility based on critical factors such as cost of energy (COE), loss of power supply probability (LPSP), and the renewable fraction (RF). The renowned particle swarm optimization (PSO) algorithm is used to optimize the system size to achieve better performance in terms of technical and economic aspects. A proper energy management technique ensures the energy balance between the demand side and the distributed energy sources. A typical 24-hour household load profile is used for the optimization.
Volume: 14
Issue: 1
Page: 127-137
Publish at: 2025-03-01

Design of flood warning prototype using ESP32 module-based ultrasonic sensors

10.11591/ijres.v14.i1.pp126-135
Arnawan Hasibuan , Muhtadi Zahiri , Misbahul Jannah , Fahrian Roid , Rizky Almunadiansyah , Armen Abta , I Made Ari Nrartha
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.
Volume: 14
Issue: 1
Page: 126-135
Publish at: 2025-03-01

Performance analysis of seven level multilevel inverter for power quality improvement

10.11591/ijape.v14.i1.pp46-54
S. Nithya Priya , K. C. Ramya
Power conversion systems for demanding applications requiring high power and power quality are increasingly using multi-level converters. Due to its many advantages, such as low harmonic content, low electromagnetic interference (EMI) output, and low power consumption in power switches, the multilayer inverter (MLI) topology is more commonly used in medium and high power applications. The chosen switching technique of the inverter for operation significantly contributes to the suppression of harmonic components while creating the optimal output voltage. A single-phase 7-level cascaded H-bridge multilevel inverter (CHB-MLI) with fewer switches and alternative control algorithms is available in MATLAB-based simulation on the SIMULINK platform. In this research, the total harmonic distortion (THD) of several control techniques is compared. From the simulation results, it was found that the proposed artificial neural network (ANN) controller outperforms the proportional-integral (PI) controller. With a lower THD value and a comparatively better sinusoidal waveform, the ANN controller produces an output voltage. It is also more suitable for improving the quality of electricity. The efficiency and performance of the proposed 7-level CHBMI system are demonstrated by the improved sinusoidal output waveform and reduced output voltage THD.
Volume: 14
Issue: 1
Page: 46-54
Publish at: 2025-03-01
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