International Journal of Reconfigurable and Embedded Systems (IJRES)
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Design of flood warning prototype using ESP32 module-based ultrasonic sensors
Get PDFNatural 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
Modeling of chimp optimization algorithm node localization scheme in wireless sensor networks
Get PDFFor 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
Artificial intelligence driven robotic control system for personalized elderly care and foot massage
Get PDFThis research presents an electronic system for providing foot massage to the elderly, along with artificial intelligence (AI) driven voice-controlled conversation bot. The problem under study focuses on the elderly age group suffering from foot related ailments, most commonly foot pain. Also, the risk of depression or anxiety is high for this age group due to social isolation. These problems are addressed by the system under discussion integrated with a voice assistant to converse with the elderly. The AI assisted conversation bot enables the elderly to make customized reminders for their timely medications and provides general updates on essential topics. The system extends to provide the elderly, foot, and calf massage controlled with mobile application. It consists of a low power motor arrangement along with a high computing system. The electronic system was subjected to trials on elderly for verification and validation of the system to assess its ability of providing users with appropriate assistance. The trials were conducted on twenty elderly, aged sixty, and above, living self-sufficiently with foot related ailment. All elderly were subjected to the conversation bot along with the foot and calves’ massage, providing subjective feedback on the system's ability to enhance their quality of life. The subjective feedback after quantification have demonstrated the ability of the system in improving their living standards
Optimizing social media analytics with the data quality enhancement and analytics framework for superior data quality
Get PDFhis paper introduces the data quality enhancement and analytics (DQEA) framework to enhance data quality in social media analytics through machine learning (ML) algorithms. The efficacy of the framework is validated through features tested against human coders on Amazon Mechanical Turk, achieving an inter-coder reliability score of 0.85, indicating high agreement. Furthermore, two case studies with a large social media dataset from Tumblr were conducted to demonstrate the effectiveness of the proposed content features. In the first case study, the DQEA framework reduced data noise by 30% and bias by 25%, while increasing completeness by 20%. In the second case study, the framework improved data consistency by 35% and overall data quality score by 28%. Comparative analysis with state-of-the-art models, including random forest and support vector machines (SVM), showed significant improvements in data reliability and decision-making accuracy. Specifically, the DQEA framework outperformed the random forest model by 15% in accuracy and 20% in true positive rate, and the SVM model by 10% in error rate reduction and 18% in reliability. The results underscore the potential of advanced data analytics tools in transforming social media data into a valuable asset for organizations, highlighting the practical implications and future research directions in this domain
Design of a dual-band bandpass filter with shunt stubs for wireless local area network and satellite communication system
Get PDFHigh-performance radio frequency (RF) modules are required in transmitter and reception devices due to the recent expansion of wireless technology. The power amplifier, low-noise amplifier, filter, and mixer are the most crucial components in the RF transmitter/receiver chain. This work presents the design and analysis of a dual-band bandpass filter (BPF) for wireless local area network (WLAN) and C-band satellite applications. Stubs of the proper electrical length that are open and short-circuited are used to implement the filter. The low pass performance is generated by the open-circuited stubs. Short-circuited stubs achieve high-pass performance, while the combination of open and short-circuited stubs achieves bandpass performance. We confirm the filter's behaviour using the advanced design system 2022 simulation tool. The findings of return loss and insertion loss confirm the simulation-level performance analysis of the filter. The result demonstrates the suggested BPF's dual-band behaviour at 4 GHz and 6 GHz
Test and measurement automation of printed circuit board assembly using digital oscilloscope
Get PDFThe testing and measurement (TM) of electrical parameters of printed circuit board assembly (PCBA) plays an integral part in the manufacturing sectors. These industries work on embedded system which widely use digital oscilloscopes (DO) for such purposes, however, operate them manually. An exponential rise in the implementation of industry 4.0 with the increasing demand for industrial products makes manual TM cumbersome. The automation of oscilloscopes (AO) remains a viable alternative to these issues requiring further investigation. An accurate and automated TM block facilitates efficient design, development, and assembly of a fully functional system hence addressed here. The AO has been carried out using generalized software that can be configured based on industry requirements. It subsequently stores the data on the server for better traceability. The automated software is developed using VB.NET and installed on a personal computer. Experiments reveal the proposed approach saves approximately 60%-70% of the time required for each PCBA operation than that of the manual system. This can enhance the productivity of the industry in terms of manpower and Resource utilization with a reduction in operating costs
Systematic review of a lightweight convolutional neural network architectures on edge devices
Get PDFA lightweight convolutional neural network (CNN) has become one of the major studies in machine learning field to optimize its potential for employing it on the resource-constrained devices. However, a benchmark for fair comparison is still missing and thus, this paper aims to identify the recent studies regarding the lightweight CNN architectures including the types of CNN, its applications, edge devices usage, evaluation types and matrices, and performance comparison. The preferred reporting items for systematic reviews and meta-analysis (PRISMA) framework was used as the main approach to collect and interpret the literature. In the process, 37 papers were identified as meeting the criteria for lightweight CNNs aimed at image classification or regression tasks. Of these, only 20 studies explored the use of these models on edge devices. To conclude, MobileNet appeared as the most used architecture, while the types of CNN focused on image classification for the general-purpose application. Following that, the NVIDIA Jetson Nano was the most utilized edge device in recent research. Additionally, performance evaluation commonly included measures like accuracy and time, along with metrics such as recall, precision, F1-Score, and other similar indicators. Finally, the average accuracy for performance comparison can serve as threshold value for future research in this scope of study
The impacts of optical display BaF2-Ce materials on solid-state lighting
Get PDFTransparent ceramic doped with barium fluorid cerium (BaF2-Ce) was created via a sintering method and its brightness and scintillation characteristics were examined. The luminescence is associated with the 5d-4f transitions in the Ce3+ ion and exhibits emitting maxima at 310 and 323 nm. For Na-22 radioisotopes, photo-maximum at 511 keV and 1274 keV were achieved using translucent ceramic BaF2-Ce. The translucent ceramic BaF2-Ce has been determined to have a power resolution of 13.5% at 662 keV. A luminescent production rate was measured for the BaF2-Ce (0.2%) ceramic, which is similar to sole crystal. Calculations of the scintillation degradation period beneath 662 keV gamma stimulation reveal a quick part of 58 ns and a somewhat sluggish part of 434 ns. The more gradual part in BaF2-Ce(0.2%) ceramic is linked to the dipole-dipole power transmission from the host structure to the Ce3+ luminous core and is quicker comparing to self-trapped excitons (STE) emitting in BaF2 host. BaF2-Ce offer various qualities, including significant illumination output, rapid degradation duration, and rapid scintillating reaction, which are desirable for many global fields such as medicine, radiation detection, industrial systems and nuclear safety
Classification metrics for pet adoption prediction with machine learning
Get PDFMillions of pets are temporarily placed in shelters, making it challenging for shelters to ensure pets find permanent homes. High adoption rates are crucial for animal welfare and the sustainability of shelter operations. This study aims to identify key factors influencing pet adoption and create classification metrics using five machine learning (ML) classification model approaches to predict the likelihood of pet adoption, to find the best model performance for each analysis. The dataset was obtained from several features related to animal characteristics and adoption conditions. The results of the study present classification of metric models that indicate decision tree and random forest (RF) as the most effective models with superior performance in terms of accuracy and class separation ability. Further research provides initial exploration of ML models that are not only limited to classification models but also model integration into internet of things (IoT) systems for the implementation of a pet adoption prediction system based on ML inference. The implementation of ML classification models helps improve the efficiency of animal adoption programs and optimize shelter operations, ultimately increasing the chances of successful pet adoption. The results of the study provide insights into factors influencing pet adoption, minimizing the length of stay (LOS) in shelters, and contribute to practitioners/ researchers as a reference for exploring new related factors and exploring the performance of ML models, especially classification models
Performance analysis of REST API in a real-time IoT-based vehicle monitoring system
Get PDFThis study studies the design and implementation of a REST API and its performance analysis for an internet of things (IoT)-based vehicles monitoring system. This system incorporates brake pad sensors, a tire pressure monitoring system (TPMS) for assessing tire pressure and temperature, light detection and ranging (LIDAR) for measuring tire thickness, and radio frequency identification (RFID) for tire identification. Data is gathered using an ESP32 microcontroller and transmitted in real-time to the server via a REST API over a wireless network. The JSON Web Token (JWT) authentication mechanism is employed to ensure data security. Testing indicates that this system has an average response time of 4–11 ms, with optimal performance recorded at 3.93 ms for the RFID sensor and peak performance at 9.19 ms for the LIDAR sensor. Load testing with 100 concurrent users demonstrates that the system maintains stability with a 100% data delivery success rate. Authentication testing demonstrates that the API is accessible solely with a valid token, hence preventing unauthorized access. This study's results demonstrate that integrating REST API with IoT monitoring systems facilitates real-time vehicle monitoring, enhances maintenance efficiency, and offers viable solutions for future predictive maintenance systems