Measurement Device of Nondestructive Testing (NDT) of Metanil Yellow Dye Waste Concentration Using Artificial Neural Network Based on Microcontroller

Dewi, A. L.; Suseno, Jatmiko Endro; Soesanto, Qidir Maulana Binu

doi:10.1109/lsens.2022.3192865

Cited by 5 publications

(2 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The third block represents the 44 matrix keypad [23] where the IC number can be entered or (AAAA) for an unidentified IC number. The fourth block is a 16×2 LCD module [24], [25] with an 11C/12C serial converter to display the IC testing results. Depending on the gate level test, the results will appear for each gate as either "OK" or "NOT OK" depending on the truth table for that gate.…”

Section: Methodsmentioning

confidence: 99%

Arduino based 74-series integrated circuits testing system at gate level

Hashim,

Awni,

Mufeed

2023

IJECE

View full text Add to dashboard Cite

<span lang="EN-US">The goal of this research article is to build and implement a low-cost, user-friendly 74-series logic integrated circuits (ICs) tester that is independent of a computer. Depending on the truth table of the gates and the IC configuration, the logic IC tester will be able to test the operation of the 74 series logic gates (AND, OR, NOR, NAND, XOR) of those ICs. It is feasible to test a range of logic ICs with higher pin widths thanks to the proposed system’s usage of an Arduino Mega platform module as a microcontroller, which provides the ability to connect 54 programmed logic inputs or outputs. The versatility offered by this design and the use of a personal computer allow for the reprograming and updating of the logic IC functional tester. Any 74-series ICs testing outcome will be shown on liquid crystal display (LCD) at the gate level. The logic IC functional tester was successfully constructed and operates flawlessly.</span>

show abstract

Section: Methodsmentioning

confidence: 99%

Arduino based 74-series integrated circuits testing system at gate level

Hashim,

Awni,

Mufeed

2023

IJECE

View full text Add to dashboard Cite

show abstract

“…A deep learning and computer vision model to forecast strawberry plant environmental sensor measurements was developed using data collected from various sensors during a 2-month period-July and August of the year 2023. The sensors used in model development were a FLIR Lepton 3.5 radiometric thermal camera [20], BH1750 light sensor [21], DHT11 temperature and humidity sensors [21,22], and a soil water content sensor [23]. The development process included several stages like data gathering, preprocessing, model development, tuning, and validation (Figure 2).…”

Section: Model Development and Trainingmentioning

confidence: 99%

Computer Vision and Machine Learning-Based Predictive Analysis for Urban Agricultural Systems

Kempelis,

Polaka,

Romanovs

et al. 2024

Future Internet

View full text Add to dashboard Cite

Urban agriculture presents unique challenges, particularly in the context of microclimate monitoring, which is increasingly important in food production. This paper explores the application of convolutional neural networks (CNNs) to forecast key sensor measurements from thermal images within this context. This research focuses on using thermal images to forecast sensor measurements of relative air humidity, soil moisture, and light intensity, which are integral to plant health and productivity in urban farming environments. The results indicate a higher accuracy in forecasting relative air humidity and soil moisture levels, with Mean Absolute Percentage Errors (MAPEs) within the range of 10–12%. These findings correlate with the strong dependency of these parameters on thermal patterns, which are effectively extracted by the CNNs. In contrast, the forecasting of light intensity proved to be more challenging, yielding lower accuracy. The reduced performance is likely due to the more complex and variable factors that affect light in urban environments. The insights gained from the higher predictive accuracy for relative air humidity and soil moisture may inform targeted interventions for urban farming practices, while the lower accuracy in light intensity forecasting highlights the need for further research into the integration of additional data sources or hybrid modeling approaches. The conclusion suggests that the integration of these technologies can significantly enhance the predictive maintenance of plant health, leading to more sustainable and efficient urban farming practices. However, the study also acknowledges the challenges in implementing these technologies in urban agricultural models.

show abstract