Color Sensor Research Articles

A self-powered wireless sweat-analysis patch for real-time monitoring physiological status

For both professional and amateur athletes, perspiration analysis technique is considered as a powerful tool for real-time monitoring the athletes’ physiological status during sports. This study presents an innovative self-powered wireless sweat-analysis patch worn on the skin for real-time monitoring chloride concentration and pH levels in perspiration. The patch consists of two main components: self-powered unit and sensing unit. The power unit, made of piezoelectric ceramic materials, converts mechanical energy during exercise into electrical energy to drive the whole system. The sensing unit has the functions of sampling and analyzing the sweat, and wireless communication. Through an exactly designed hydrophobic and hydrophilic composite microchannel structure, sweat is effectively guided to the storage chamber. The colorimetric fabrics embedded in the chamber respond to the presence of chloride ions and pH levels in the sweat, producing a color-changing reaction. Color sensors above the fabrics detects these color changes for analyzing the sweat, while Near Field Communication (NFC) technology wirelessly transmits this information to outside mobile devices or data-collection cloud. This system not only provides a new avenue for real-time monitoring of athletes’ health status but also offers a scientific basis for adjusting training plans.

Feasibility of Using a Cheap Colour Sensor to Detect Blends of Vegetable Oils in Avocado Oil.

This proof-of-concept study explored the use of an RGB colour sensor to identify different blends of vegetable oils in avocado oil. The main aim of this work was to distinguish avocado oil from its blends with canola, sunflower, corn, olive, and soybean oils. The study involved RGB measurements conducted using two different light sources: UV (395 nm) and white light. Classification methods, such as Linear Discriminant Analysis (LDA) and Least Squares Support Vector Machine (LS-SVM), were employed for detecting the blends. The LS-SVM model exhibited superior classification performance under white light, with an accuracy exceeding 90%, thus demonstrating a robust prediction capability without evidence of random adjustments. A quantitative approach was followed as well, employing Multiple Linear Regression (MLR) and LS-SVM, for the quantification of each vegetable oil in the blends. The LS-SVM model consistently achieved good performance (R2 > 0.9) in all examined cases, both for internal and external validation. Additionally, under white light, LS-SVM models yielded root mean square errors (RMSE) between 1.17-3.07%, indicating a high accuracy in blend prediction. The method proved to be rapid and cost-effective, without the necessity of any sample pretreatment. These findings highlight the feasibility of a cost-effective colour sensor in identifying avocado oil blended with other oils, such as canola, sunflower, corn, olive, and soybean oils, suggesting its potential as a low-cost and efficient alternative for on-site oil analysis.

Phytoplankton optical fingerprint libraries for development of phytoplankton ocean color satellite products

Phytoplankton respond to physical and hydrographic forcing on time and space scales up to and including those relevant to climate change. Quantifying changes in phytoplankton communities over these scales is essential for predicting ocean food resources, occurrences of harmful algal blooms, and carbon and other elemental cycles, among other predictions. However, one of the best tools for quantifying phytoplankton communities across relevant time and space scales, ocean color sensors, is constrained by its own spectral capabilities and availability of adequately vetted and relevant optical models. To address this later shortcoming, greater than fifty strains of phytoplankton, from a range of taxonomic lineages, geographic locations, and time in culture, alone and in mixtures, were grown to exponential and/or stationary phase for determination of hyperspectral UV-VIS absorption coefficients, multi-angle and multi-spectral backscatter coefficients, volume scattering functions, particle size distributions, pigment content, and fluorescence. The aim of this publication is to share these measurements to expedite their utilization in the development of new optical models for the next generation of ocean color satellites.

Iodine Gas Detection System Using Color Sensor

Iodine can exist in the form of radioactive and non-radioactive states. Radioactive Iodine-131 (I-131) has been utilized in nuclear facilities. Due to its ability to be absorbed by the thyroid gland, I-131 is used for therapy in thyroid cancer cases. Contrary to the benefit, Iodine is a volatile material that becomes a health problem if humans inhale more than a certain dose under a certain period. Thus, to prevent overexposure to Iodine, all Iodine-related facilities must equip their facilities with Iodine detection systems. The most common radioactive detection system uses an expensive Na(Tl), and HPGe detector. So, this study will build a low-cost iodine detection system by means of a low-cost color-based sensor utilizing the color property of the Iodine gasses. This finding showed that the color sensor will detect the Iodine gas leaks in the air in a range of 0 - 100 ppm, resulting in a pink-dark purple color. This finding showed that the sensor could detect low concentrations of Iodine gas in particular areas very well. Since the good resolution of this detection system, it will be promising to be applied in a real-time iodine gasses detection system.

Experimental demonstration of an indoor visible light positioning system using RGB LEDs for multi‐cell networks

AbstractA visible light positioning (VLP) system for multi‐cell networks based on multi‐colour LED that combines frequency division multiplexing with the received signal strength and a trilateration method is proposed. First, it employs experimental measurements to model the designed visible light communication (VLC) system under the characteristics of the target scenario. Second, it introduces a low‐cost VLC transmitter that exploits the chromatic space to transmit the VLP information. Third, it characterizes the VLC transmitter and proposes a linearization for electro‐optical responses of RGB LED. An RGB digital colour sensor and simple method calibrate the chromatic emission. The experimental results show that the proposed VLP multi‐cell architecture achieves a positioning accuracy lower than .

Compared RGB Methods Towards Efficient Money Detector for Blind People

Limitations of profound visual impairment distinguishing each nominal number of banknotes are often used by people with bad intentions to take advantage of that basis, like money fraud. Due to this reason, the blind people need to be helped to recognize their surroundings by developing assistive technology that is advanced for them. This study aims to build an efficient design of a money detector by comparing three RGB methods: range breakdown, If-Then Rules, and decision tree to recognize the nominal of money. The sample used in this experiment is rupiah banknotes for the 2016 and 2022 issuances. The device is built with a TCS3200 colour sensor and designed in a real-time platform. It has been found that the highest average percentage accuracy was achieved by the breakdown range method with 100% (2016 sample) and 90% (2022 sample). This device also successfully produced a notification sound from a speaker that mentions the detected nominal value. This research could be used as a reference to improve assistive technology for blind people.

Thermally programmable time delay switches for multi-step assays in paper-based microfluidics

Paper-based microfluidic devices offer advantages such as low cost and disposability for point-of-care diagnostic applications. However, actuation of fluids on paper can be a challenge in multi-step and complex assays. In this work, a thermally programmable time-delay switch (TPTDS) is presented which operates by causing delays in the fluid path of a microfluidics paper-based analytical device (μPAD) by utilizing screen-printed wax micro-bridges. The time-delay is achieved through an electrical power feedback loop which indirectly adjusts the temperature of each individual micro-bridge, melting the wax into the paper. The melted wax manipulates the fluid flow depending on its penetration depth into the paper channel, which is a function of the applied temperature. To demonstrate functionality of the proposed method, the TPTDS is employed to automate and perform the nitrate assay which requires sequential delivery of reagents. Colorimetric detection is used to quantify the results by utilizing an electronic color sensor.

Using an inexpensive RGB color sensor for field quantitative assessment of soil accessible Cu(Ⅱ)

Field rapid determination of soil accessible Cu(Ⅱ) was important for environmental safety and human health risk assessment. In this study, an inexpensive red, green, and blue (RGB) color sensor was used for quantitative color difference analysis of the colored solution for soil accessible Cu(Ⅱ) with bis-cyclohexanone oxalydihydrazone as color reagent to develop a new method for analyzing soil accessible under field conditions. First, the calibration curve for RGB color sensor method was established in the standard solutions of Cu(II). Then the “hand shaking + standing” field extraction method for accessible Cu(Ⅱ) was developed. Finally, the method was applied in contaminated soils in the laboratory and in the field, and set the values determined by atomic absorption spectroscopy (AAS) as the standard ones. Results indicated that in the range of 0.1–5 mg L−1 Cu(II), the RGB Euclidean chromogenic difference values were directly linear correlated with the concentration of Cu(II) (R2 > 0.999). The interference of Fe(Ⅲ) and Mn(Ⅱ) could be eliminated by adding citric acid. The “hand shaking + standing” field extraction method could effectively extract the accessible Cu(Ⅱ) from soil with the high extraction rates. The concentrations of accessible Cu(II) in various polluted soils determined by RGB color sensor method were consistent with that determined by AAS, with the relative error within ±5%, the relative standard deviation ≤ 20%. The recovery of Cu(II) in RGB color sensor method was between 97% and 105%, which could meet the requirements of trace analysis of accessible Cu(Ⅱ) in the field. The high accuracy and precision of RGB color sensor method was reconfirmed in the rapid field quantitative assessment of soil accessible Cu(Ⅱ). Due to that the RGB color sensor was low cost, rechargeable, portable, mobile, ambient light resistant, the method would have a great potential for the determination of accessible Cu(Ⅱ) in contaminated soils.

Chromaticity measurement of airport navigation lighting using integrated colour sensor

Chromaticity measurement of airport navigation lighting using integrated colour sensor

Rancang Bangun Detektor Kematangan Buah Kelapa Sawit Menggunakan Sensor Tcs3200 Dan Modul Wifi ESP32-CAM dengan Notifikasi via Telegram

This study aims to make a detector of oil palm fruit maturity using the TCS3200 color sensor and the ESP32-CAM Wi-Fi module with notifications via Telegram. Oil palm farmers need a tool to detect the ripeness of the fruit to make it easier for them to monitor the fruit. This tool uses the TCS3200 color sensor to detect the ripeness of palm fruit based on the color of the palm skin; at the same time, the ESP32-CAM module is useful for processing data from the TCS3200 color sensor as the output of Telegram notifications. The TCS3200 color sensor detects the RGB value of the palm object, which is then processed by the ESP32-CAM module. The results obtained in testing the maturity detection tool for palm fruit in ripe and immature conditions can be seen from the Telegram notification. The RGB value of the TCS3200 color sensor will be displayed as information on the condition of the palm fruit, which is detected and used as a notification on the Telegram application. The highest percentage of success in testing the maturity of the fruit was when the fruit was immature at 100%, and the lowest was when the fruit was ripe at 86.6%.

A colour sensor integrated into a microcontroller platform as a reliable tool for measuring pH changes in biochemistry applications.

Colorimetry is a widely used technique for optical detection in point-of-care testing and on-site detection. Although some studies employ a multiplex approach to analyse coloured solutions, many still analyse one sample at a time. We have prepared a simple and affordable colorimetric assay based on a TCS34725 colour sensor (ams-OSRAM) integrated into an M5Stack module and an RGB LED module both inserted into a 3D printed frame. We found that the colorimetric assay can be easily transferred to a colour sensing platform, and the signal range obtained using the prepared colorimeter is more than 200 times larger than that obtained using digital image colorimetry (DIC) for the same samples containing cholinesterase or urease as a model enzyme providing a change in pH of the processed solution. The assay appears to be ready for practical use.

Wavelength-scanning pixel-super-resolved lens-free on-chip quantitative phase microscopy with a color image sensor

We report a wavelength-scanning-based lens-free on-chip microscope using a color CMOS sensor and a matching modified phase retrieval algorithm for pixel super-resolution. Compared to traditional monochrome industrial cameras, color sensors favored by the consumer electronics industry have smaller pixel sizes, higher performance, and lower costs. However, the color filtering array (CFA) introduces inherent modulation to the holograms acquired under quasi-monochromatic illumination, which complicates the data processing in lens-free on-chip microscopy. Without physically removing the CFA positioned on the sensor chip, we demonstrate quantitative phase imaging (QPI) with a lateral half-width resolution of 615 nm over a wide field-of-view of 51.88 mm2 by exploiting the green-channel data from Bayer-masked holograms. The resulting spatial bandwidth product is 137.2 megapixels, over 10 times that of a conventional optical microscope. The rationale for using only green-channel data is that the information from each sampling point is not lost during propagation but rather distributed to all pixels in the image. Therefore, the missing data in other channels can be recovered by exploiting the sufficient differences among the raw images captured at different wavelengths. Compared to the scheme with monochrome sensors, this method requires the acquisition of several more images to guarantee the convergence of the algorithm. Experimental results show that we can achieve high-quality QPI performance, thus demonstrating the applicability of cost-effective color sensors in the field of lens-free holographic microscopy.

Advanced design and Engi-economical evaluation of an automatic sugarcane seed cutting machine based RGB color sensor.

There are many problems related to the use of machine learning and machine vision technology on a commercial scale for cutting sugarcane seeds. These obstacles are related to complex systems and the way the farmers operate them, the possibility of damage to the buds during the cleaning process, and the high cost of such technology. In order to address these issues, a set of RGB color sensors was used to develop an automated sugarcane seed cutting machine (ASSCM) capable of identifying the buds that had been manually marked with a unique color and then cutting them mechanically, and the sugarcane seed exit chute was provided with a sugarcane seed monitoring unit. The machine's performance was evaluated by measuring the damage index at sugarcane stalk diameters of 2.03, 2.72, 3.42, and 3.94 cm. where two different types of rotary saw knives had the same diameter of 7.0 in/180 mm the two knives had 30 and 80 teeth, also we used five cutting times of 1000, 1500, 2000, 2500, and 3000 ms. All tests were done at a fixed cutting speed of 12000 rpm. In addition, the machine's performance was evaluated by conducting an economic analysis. The obtained results showed that the most damage index values were less than 0.00 for all cutting times and sugarcane stalk diameters under testing, while the DI values were equal zero (partial damage) for sugarcane stalk diameter of 3.42 cm at cutting times of 2000 ms and 2500 ms, in addition to the DI values being equal zero (extreme damage) for sugarcane stalk diameter of 3.94 cm at cutting times of 1500 ms and 2000 ms. The economic analysis showed that the total cost of sugarcane seeds per hectare is 70.865 USD. In addition, the ASSCM can pay for itself in a short period of time. The payback time is 0.536 years, which means that the ASSCM will save enough money to pay for itself in about 6.43 months. Finally, we suggest using a rotary saw knife with 80 teeth and a cutting time of 2000 ms to cut sugarcane stacks with an average diameter of 2.72 cm. This will result in higher performance and lower operating costs for the ASSCM.

Construction of a Radiometric Degradation Model for Ocean Color Sensors of HY1C/D

Construction of a Radiometric Degradation Model for Ocean Color Sensors of HY1C/D

Machine Learning Evaluation of Fruit Ripeness with Multidimensional Sparse-Dataset Calibration

HighlightsThis article provides a machine-learning method for estimation of sugar content in target fruit using color appearance.Complex networks based on multidimensional datasets work effectively for color sensor calibration and data rebalancing.Beyond datapoint sparsity in datasets, estimation accuracy in sugar content is assured when an unsupervised-learning algorithm classifies the multidimensional datapoints composed of fruit color and relevant quantities.Abstract. In agriculture, harvests of fruits and vegetables depend on their ripeness, in which farmers should rely not only on the eye-sensed outlook but rigorously on constituents inside the targets, like sugar content. So far, detail measurements on the outlook color of fruits have been widely reported to detect suitable harvest times, where non-destructive instruments, which are costly in general agriculture fields, work well for color estimation whose resolution level is much higher than human eyes. In this study, we propose a scheme with a calibration procedure for sugar content prediction from outlook-color quantities, which is applicable even if a sensor includes systematic errors in output signals or the base dataset includes some datapoint sparsity. Most multidimensional data, which are both in agriculture and in other general cases, are imperfect in terms of several aspects, but they can be useful when we take appropriate sparsity detection and setting of rebalancing weights into account for machine learning procedures. We newly introduce a complex-network method for this purpose, and after the color calibration, we successfully obtain accurate hue and chroma, which are quantities representing a position on calibrated color coordinate, to predict °Brix sugar content in Japanese pears. Keywords: Asian pear, Color Elements, Fruit Ripening, °Brix Sugar Content, OHD.

Prototype of a device adaptable to walk-sticks with a system to communicate the visible spectrum to blind people.

This project aims to design and build a device adaptable to canes for blind people that allows them to know the color of everyday objects through a tactile response. The prototype of the device identifies the colors by means of the TCS3200 color sensor that, when detecting the light reflected by the objects, converts it into an electrical signal to later process it with the Arduino Nano microcontroller and thus generate a tactile response using a servomotor. For this purpose, mechanical and electronic designs of the color recognition and representation systems using the Feelipa color code were developed, and a functional prototype of the adaptive device that identifies primary colors was developed. Algorithms for the classification of secondary colors from their RGB values continue to be developed, and the mechanical designs are being improved to take into account aesthetic and ergonomic aspects that facilitate their use

A detection system for serum cholesterol based on the fluorescence color detection of beta-cyclodextrin-capped gold nanoclusters

Cholesterol is one of the major markers for cardiovascular diseases. Herein, a portable cholesterol measurement system based on fluorescence color detection was constructed by combining the high sensitivity of fluorescence analysis with the ease of color sensing to determine low levels of serum cholesterol. Cyclodextrin capping gold nanoclusters with blue-green emission were used as fluorescent probes because cholesterol exposure induced fluorescence enhancement of the probe due to the host–guest inclusion interaction between cholesterol and the cavity of cyclodextrin. The integrated sensing system consisted of modules including a microprocessor, a power supply, an LED light with a constant current source, an RGB color sensor, a display, and a darkroom. All the modules except the display screen were placed in a 3D printing darkroom to avoid interference from ambient light. An RGB color sensor TCS230 was applied to capture the RGB signals of the fluorescent color of the probe solution before and after cholesterol addition. Then the obtained RGB signals were converted into the signals in Hue, Saturation, and Value (HSV) color space with a central control chip STM32F407. The Hue value of the fluorescent color of the solution can discriminate the concentration change of cholesterol. Experimental results demonstrate that the system responds linearly to cholesterol in the concentration range of 20.00 ∼ 150.00 μmol·L-1 with a detection limit of 16.07 μmol·L-1 (3σ, n = 3). The detection of the system has good consistency and accuracy compared with the standard instrument, showing potential for the detection of low levels of serum cholesterol.

Development Of Waste Sorting Technology for Pet and HDPE Plastic Bottles For The Production Of Recycled Plastic Seeds

Plastic bottle waste is a problem that always arises. In Indonesia, the processing of plastic bottle waste still needs to be improved. Plastic bottle waste can be converted into plastic pellets with economic value. The type of plastic and the colour must be sorted to get quality plastic pellets. A machine is designed to sort out the types of plastic bottle waste, polyethene terephthalate (PET) and high-density polyethene (HDPE). The result is a prototype plastic biotol waste sorting machine with a 41 kg/hour capacity. The prototype of this machine can sort out plastic bottle waste and those not included in the plastic bottle waste category. The results show that the colour sensor and object sensor work optimally to sort plastic bottle waste according to the program.

IoT based Siddha diagnosis for human health monitoring

In Siddha, the process of diagnosis is completely related to physical and clinical metrics of the human body. The art of diagnosing and the prognosis are conveyed by the siddhars by using silk or twine thread, which is an ancient technique followed till now to diagnose diseases in human. This paper proposes an IoT based automated device to detect the disease using siddha methodologies. The aim is to design a novel non-invasive device to identify human health problems using the carpus (wrist) circummetric sign. This device is developed and integrated on the basis of IoT platform for real time usage. It determines the wrist circummetric sign, condition of person health to be measured automatically using IR sensor, color sensor, actuators. Here, we inbuilt the embedded IoT system for validating the accurate results for better performance and display the output results in mobile through MQTT dashboard. This developed human health monitoring device can be used for diagnosing human physiological based parameters related to siddha which is termed as Manikadainool. Results proved that this device can predict the wrist parameters for Manikadainool in accurate and efficient manner.

Design of Digital Control System for Line Following Robot

Design of mobile robots has become an increasingly growing trend in the technology of modern times. They are very attractive engineering systems, not only because of many interesting theoretical aspects concerning kinematics, intelligent behavior and autonomy, but also because of applicability in many human activities. A typical example is the line following robot (LFR). In order for a LFR to function effectively, it must demonstrate excellent line tracking control. This is achieved by having accurate and responsive control algorithms as well as high precision color sensor systems. This paper proposes a system to show that good line tracking performance can be achieved with moderately simple digital control algorithms. The platform used is a differentially driven wheeled robot constructed using the Lego Mindstorms components. The simulation models are presented and analyzed using MATLAB Simulink . The main programming environment is the EV3 Software.