A Comparison Study of the Optical Measurement Sensitivity of the Phototransistor and Photodiode

Özçelik, Mehmet Ali

doi:10.29137/umagd.419662

Cited by 4 publications

(4 citation statements)

References 10 publications

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“…Meanwhile, sensitivity was taken from each component standard deviation value. This deviation means an electrical response concerning light, where a better variation profile in terms of sensitivity and precision [22]. For the deviation value for each sensor, the authors would like to propose using Wendy's and colleague's research results [23].…”

Section: Resultsmentioning

confidence: 99%

Sensor Selection Comparison Between Fuzzy Topsis Algorithm and Simple Additive Weighting Algorithm in Automatic Infuse Monitoring System Application

Budiyanto

Hakim

Firdausi

et al. 2020

Sinergi

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One of the critical equipment to support a patient in the hospital would be an infuse system. One of the main problems with the infuse system was manual monitoring. Few researchers try to build a low cost infuse system using a low-cost sensor and microcontroller. This paper proposes a fuzzy Topsis algorithm and Simple Additive Weighting (SAW) algorithm to choose the best sensor for a low cost to the infuse system, which is one of the Multiple Criteria Decision Making (MCDM) problems. Several simulations using three sensors, such as LDR (photoresistor), phototransistor, and photodiode, are performed. By using these two algorithms, it can be shown that the phototransistor emerges as the best sensor with value 1, even though it has the price six times higher from the LDR sensor and three times higher from the photodiode.

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Section: Resultsmentioning

confidence: 99%

Sensor Selection Comparison Between Fuzzy Topsis Algorithm and Simple Additive Weighting Algorithm in Automatic Infuse Monitoring System Application

Budiyanto

Hakim

Firdausi

et al. 2020

Sinergi

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“…Both current outputs showed linearity, but the RGB color sensor current output has greater linearity and uniformity in response to variable illumination than the phototransistor. The sensitivity of the sensors was taken from the current standard deviation value of each sensor [19]. It is observed that both sensors respond exponentially to light, but while the value of the standard deviation of the RGB color sensor is 20.26, the phototransistor is 30.52.…”

Section: Performance Analysismentioning

confidence: 99%

IoT Based Smart Palm Oil Seed Segregator using RGB Color Sensor

Izzatul Nadia Azhar,

Azlina Idris,

Nugrahini Susantinah Wisnujati

et al. 2024

ARAM

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The manual seed grading process through human vision is tedious and time-consuming due to the tendency for errors and inconsistencies. Shell residues also failed to be properly segregated and required a huge amount of human energy as they were unable to be detected automatically once the waste container was full. The main objectives of this research are to develop an automated palm seed grading and sorting system to increase the seed’s sorting quality. The system can segregate palm oil and shell residues automatically to ease kernel recycling in the future. In addition, the system can implement the waste bins’ level detection, which can notify the user via the Blynk application, reducing the time and manpower required. To develop this system, sensor comparisons are done to determine the best-performing sensor to be used in the operation. The result shows that the RGB color sensor is the best color detecting sensor with an increment accuracy of 30.525%. As for the smart bin for shell residue, system response became 62.96% faster with the use of an ultrasonic sensor. The RGB sensor detects seed with readings <165 color concentration as freshly ripe and readings >165 color concentration as overripe. The janitor can be notified in real-time when the bins for oil and shell residues are full through the Blynk application using the WiFi module (ESP8266). The system has 100% accuracy, which was tested using the confusion matrix formula for both seed categories.

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“…In many studies in the literature, there are lux meter circuits created by using LDR [10][11][12][13]. For the calibration of this type of circuits, generally 'real/professional' lux meters are used [13,14].…”

Section: Introductionmentioning

confidence: 99%

“…It can communicate with other devices on the same network over the internet browser. When the studies in the literature were examined in detail, it was seen that the resistance of the LDR was read for calibration in many studies and these data were collected manually with multimeters [12,14]. This type of data recording method is limited by the accuracy of the multimeter and is generally performed with low precision.…”

Section: Introductionmentioning

confidence: 99%

Calibration of an LDR-Lux meter using a smartphone

Çoramık¹

2021

Phys. Educ.

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In this study, it is aimed to calibrate an LDR-Lux meter with a calibration setup which includes a light bulb whose brightness can be changed by using a dimmer circuit, a data acquisition card, a smartphone and a lux meter application. In order to make the measurements more accurate and to prevent errors in the measurements, a smartphone and a laptop were connected to each other via internet browser and the data obtained with physical phone experiments was displayed on the computer screen instantly. In the experimental setup, the brightness of the light was changed with the dimmer circuit and height adjustment. The data were obtained as a result of 32 experiments for different illumination values. As a result of this study, it is seen that the calibrated experimental setup works with high accuracy. With this experimental setup, it is thought that students can also easily test the inverse square law of light. The main factors that make this experimental setup effective are that, unlike other studies in the literature, a professional lux meter is not used and the cost of the developed LDR-Lux meter is very low.

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A Comparison Study of the Optical Measurement Sensitivity of the Phototransistor and Photodiode

Cited by 4 publications

References 10 publications

Sensor Selection Comparison Between Fuzzy Topsis Algorithm and Simple Additive Weighting Algorithm in Automatic Infuse Monitoring System Application

Sensor Selection Comparison Between Fuzzy Topsis Algorithm and Simple Additive Weighting Algorithm in Automatic Infuse Monitoring System Application

IoT Based Smart Palm Oil Seed Segregator using RGB Color Sensor

Calibration of an LDR-Lux meter using a smartphone

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