Color Sensor

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Application Of Color Sensor In An Automated System.

Application Of Color Sensor In An Automated System.

With the use of TCS230 Color Sensor, Basic Stamp programmer and BS2P microcontroller, this project explores the possibility of creating a programming that can sort RGB colors. In this project, the main objective is to create program that can identify red, green and blue colors and fabricate a mechanical system for identify RGB color by using a conveyor. The other objective also includes the understanding of the application of color sensor in an automated system by related literatures review.

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Digital Camera Based Color Sensor for Determining Leaf Color Level of Paddy Plants

Digital Camera Based Color Sensor for Determining Leaf Color Level of Paddy Plants

The color sensor developed in this research mainly consists of a CCD camera, a magnet to trigger the camera, a laptop computer to save and process the image, a leaf color chart as the color reference, and a cart to manually transport the sensor across the field. The sensor has been able to determine the color level of paddy plants with the accuracy 39% for color level 2, 62% for color level 3, and 66% for color level 4. The low accuracy is mainly caused by inconsistency of the camera in capturing the colors and the vibration during the transport. The wheel design also rises a problem as it sometimes doesn’t rotate in the mud so that the magnet doesn’t trigger the camera.
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Electrical and Optical Properties of Photodetector Based On BST Thin Film Doped Ta2O5 as Color Sensor.

Electrical and Optical Properties of Photodetector Based On BST Thin Film Doped Ta2O5 as Color Sensor.

Hasil sintesis film tipis BST di karakterisasi arus-tegangan (I-V), konduktivitas listrik, sifat optik mencakup absorbansi dan reflektansi, indeks bias, energi gap, konstanta dielektrik dan analisis mikrostruktur BST thin film berupa XRD dan SEM-EDX. Berdasarkan hasil karakterisasi I-V terlihat adanya pergeseran pada masing-masing kurva I-V. Sehingga dapat disimpulkan bahwa film tipis BST memiliki respon terhadap panjang gelombang sinar tampak sehingga dapat diaplikasikan sebagai sensor warna. Karakterisasi optik film tipis menunjukkan bahwa semakin besar persentase pendadah, maka semakin kecil nilai persen absorbansi film tipis. Hal ini berkaitan dengan energi gap film tipis yang akan semakin menurun seiring dengan bertambahnya persentase pendadah. Penurunan energi gap ini disebabkan oleh munculnya level baru diantara level valensi dan level konduksi yang dibentuk oleh pendadah Ta 2 O 5 sehingga
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RANCANG BANGUN ALAT GRADING BUAH TOMAT (Solanum lycopersicum, L) MENGGUNAKAN SENSOR WARNA TCS230 Design of Tomato Grading Equipment (Solanum Lycopersicum, L) Using Color Sensor Tcs230

RANCANG BANGUN ALAT GRADING BUAH TOMAT (Solanum lycopersicum, L) MENGGUNAKAN SENSOR WARNA TCS230 Design of Tomato Grading Equipment (Solanum Lycopersicum, L) Using Color Sensor Tcs230

Penelitian ini bertujuan untuk (a) melakukan perancangan sistem alat grading buah tomat untuk mengetahui kerja alat dan keakuratan berdasarkan ukuran warna, (b) merancang sistem otomatis rangkaian elektronika dan bahasa program untuk alat grading buah tomat menggunakan sensor warna TCS230, (c) melakukan pengujian kinerja alat grading buah tomat berdasarkan ukuran warna buah tomat. Metode yang digunakan pada penelitian ini adalah metode eksperimen terdiri dari identifikasi masalah, investarisasi ide, penyempurnaan ide, prinsip kerja, rancangan fungsional, rancangan struktural, tahap perakitan, dan tahap uji kinerja alat. Rancang bangun yang dilakukan dapat menghasilkan alat grading buah tomat menggunakan sensor warna TCS230 gabungan hasil rancangan mekanik dan rangkaian elektonika sistem otomatis dengan prinsip kerja. (1) alat disambungkan dengan sumber listrik. (2) buah tomat dimasukan pada ruang scannimg melalui hopper (3) sensor warna TCS230 membaca warna buah tomat. (4) warna buah tomat ditampilkan pada LCD. (5) motor central lock mendorong buah tomat. (6) motor servo MG996R menggerakkan pintu keluaran ke kanan jika yang terbaca tomat warna hijau dan ke kiri jika tomat warna merah. (7) motor servo SG90 membuka portal pintu keluaran dan buah tomat keluar. Pengujian alat grading menggunakan sensor warna TCS230 mendekati akurat karena nilai koefisien determinasi 0.8747 untuk R, 0.9646 untuk nilai G, dan 0.8538 untuk nilai B. Penentuan kelas buah tomat juga mendekati maksimal, hanya 3.333% kesalahan dalam pembacaan kelas buah tomat.
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PENDETEKSI KEMURNIAN BENSIN C8H18 DAN C10H24 DI SPBU PERTAMINA BERBASIS SENSOR WARNA PORTABEL PETROL PURITY C8H18 AND C10H24 DETECTOR AT SPBU PERTAMINA BASED ON COLOR SENSOR PORTABLE

PENDETEKSI KEMURNIAN BENSIN C8H18 DAN C10H24 DI SPBU PERTAMINA BERBASIS SENSOR WARNA PORTABEL PETROL PURITY C8H18 AND C10H24 DETECTOR AT SPBU PERTAMINA BASED ON COLOR SENSOR PORTABLE

Alat portabel yang akan dibuat menggunakan sensor TCS3200 untuk membedakan warna dengan cara mengidentifikasi warna berdasarkan nilai RGB (Red, Green, Blue) yang membentuk warna dari bensin tersebut. Nilai RGB tersebut akan digunakan sebagai parameter kemurnian bensin yang dideteksi. Penulis akan mengendalikan alat portabel ini dengan menggunakan Arduino Uno berbasis mikrokontroler ATmega328 yang diisikan program untuk membaca nilai RGB dari bensin yang diarahkan pada sensor TCS3200 kemudian disimpan di dalam EEPROM Arduino Uno. Data nilai RGB yang tersimpan selanjutnya akan digunakan sebagai acuan untuk mengenali beberapa produk bensin yang diarahkan pada sensor TCS3200.
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Segway Line Tracer Using Proportional-Integral-Derivative Controllers

Segway Line Tracer Using Proportional-Integral-Derivative Controllers

In the subsequent testing process, testing the performance of the color sensor. Color sensor is used to detect the color of the track under the robot to recognize the tracks. In the following image readings color configuration is done by robots. This test is used to read the success rate sensor readings of color when recognizing six colors including: black (color path), white (environment path) as well as red, yellow, green and blue is the color disturbance is located between the environment and pathways such as the Figure 6.
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Grendy Christopher(grendy_christopheryahoo.com), Riduan Wijaya (he5_hahayahoo.com) Dedy Hermanto (dedi.triesgmail.com), Eka Puji Widianto (ekapujiw2002gmail.com) Jurusan Teknik Informatika STMIK GI MDP

Grendy Christopher(grendy_christopheryahoo.com), Riduan Wijaya (he5_hahayahoo.com) Dedy Hermanto (dedi.triesgmail.com), Eka Puji Widianto (ekapujiw2002gmail.com) Jurusan Teknik Informatika STMIK GI MDP

Pada fase ini penulis melakukan pembuatan perangkat keras dari skematik yang sudah dirancang pada fase sebelumya. Perangkat keras yang dibuat meliputi mikrokontroler, RGB Color Sensor, dan Power Supply. Selain membuat rangkaian elektronika tersebut penulis juga membuat lengan robot beserta komponen-komponen pendukung lainnya. Setelah semua komponen selesai dibuat, penulis melakukan pengintegrasian terhadap komponen- komponen tersebut hingga menjadi satu kesatuan sistem. Penulis juga melakukan pengkodean terhadap robot yang telah selesai dibuat serta menyertakan flowchart yang menjelaskan bagaimana robot ini bekerja. 5. Testing
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Smart Color Sorting Robot.

Smart Color Sorting Robot.

In this project a line follower will brought the ball that will be sense by LDR sensor to right station. A main part in this project is an LDR sensor. Commonly this type of sensor used to detect light. Other applications are for color sensor, line detector, and switching element. Capability of this system to detect the type of this object and it will be chosen based on their color.

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Adobe Photoshop 6 Bible

Adobe Photoshop 6 Bible

Photoshop calculates the first value by multiplying the height and width of the image (both in pixels) by the bit de pth of the image, which is the size of each pixel in mem- ory. Consider a typical full-color, 640 × 480-pixel image. A full-color image takes up 24 bits of memory per pixel (which is why it’s called a 24-bit image). There are 8 bits in a byte, so 24 bits translates to 3 bytes. Multiply that by the number of pixels and you get 640 × 480 × 3 = 921,600 bytes. Because there are 1,024 bytes in a kilobyte, 921,600 bytes is exactly 900K. Try it yourself — open a 640 × 480-pixel RGB image and you’ll see that the first number in the preview box reads 900K. Now you know why. But it’s the second value, the one that factors in the layers, that represents the real amount of memory that Photoshop needs. If the image contains one layer only, the numbers before and after the slash are the same. Otherwise, Photoshop measures the opaque pixels in each layer and adds approximately 1 byte of overhead per pixel to calculate the transparency. The second number also grows to accommodate paths, masks, spot-color channels, undoable operations, and miscellaneous data required by the image cache.
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Inhomogenous Color Object Recognition Using Monochromatic-Based Technique With Color Camera.

Inhomogenous Color Object Recognition Using Monochromatic-Based Technique With Color Camera.

A color image requires three separate items of information for each pixel and they represent value levels of the information given. For RGB images, the images are built from three separate channels for red, green and blue. Each pixel has intensities from 0 (black) to 255 (white) and each pixel eight bit and a total of 255³=16,777,216 represent any possible color in the image as 24-bit color [7]. The image also consists of a three matrices representing the value of red, green and blue for each pixel. The resulting of grayscale images from each channel produces similar brightness. The red channel has a brighter part in red part and caters the contrast map, green channel has much brighter part in green part and shown to have more sources detail and blue channel has much brighter in blue part and take over for the noise [8]. From objectives as illustrated in Chapter 1, this color images produced a three gray-scaled images from three color components; red, green and blue color components and its combination; RB, RG and GB. The results produced the most significant and less significant color channels on color images based on contrast, recognition rate and speed to recognize object. The image with an m-by-n frame size represented by m x n x 3 in MATLAB.
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Application of Color Segmentation To Help Partial Color Blind People - Politeknik Negeri Padang

Application of Color Segmentation To Help Partial Color Blind People - Politeknik Negeri Padang

There is also a name this wavelength as RGB (red, green, and blue) however, the naming of SML is more appropriate. In conical cells, there are 3 types that display the color, while the stem cell is only one type, indicating that the stem cell is not able to identify the color. The S cell is spread evenly over the entire retina, but not in the center of the fovea. The ratio of L: M: S is 12: 6: 1 [3].

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Create Meaningful Graphics, Icons, and Images Choose the Proper Colors

Create Meaningful Graphics, Icons, and Images Choose the Proper Colors

 Color must always have a meaningful purpose  Use the browser 216-color palette  Presentation:  Minimize the number of presented colors  Always consider color in context  Use s[r]

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OPTIMASI SENSOR KAMERA PADA PROSES IDENTIFIKASI WARNA DENGAN PENGOLAHAN CITRA MENGGUNAKAN DESIGN OF EXPERIMENT OPTIMIZATION SENSOR CAMERA IN COLOR IDENTIFICATION PROCESS WITH IMAGE PROCESSING USING DESIGN OF EXPERIMENT

OPTIMASI SENSOR KAMERA PADA PROSES IDENTIFIKASI WARNA DENGAN PENGOLAHAN CITRA MENGGUNAKAN DESIGN OF EXPERIMENT OPTIMIZATION SENSOR CAMERA IN COLOR IDENTIFICATION PROCESS WITH IMAGE PROCESSING USING DESIGN OF EXPERIMENT

Kamera merupakan salah satu sensor dari robot. Kamera sangat sensitif terhadap faktor lingkungan yang sering berubah-ubah, sehingga dibutuhkan suatu penelitian terhadap faktor yang berpengaruh pada kinerja kamera dan pengaturan kombinasi faktor untuk meminimalkan error rate dalam mengidentifikasi citra. Untuk menyelesaikannya dibutuhkan tahap design of experiment dengan pendekatan Taguchi menggunakan deteksi warna HSV pada pengolahan citra. Kelebihan metode Taguchi ialah mampu meminimalkan akibat dari variasi terhadap respon serta eksperimen dapat dilakukan dengan efisien. Sedangkan deteksi warna HSV memiliki dimensi warna yang cukup bervariasi. Analisa data dilakukan berdasarkan karakteristik “smaller is better” dari Signal to Noise Ratio (S/N), uji normalitas, dan analisis varians (ANOVA). Hasil analisa terhadap rasio S/N pada palet berwarna merah optimal dengan kombinasi faktor resize (120%) dengan nilai rasio S/N sebesar 13,774, resolusi kamera (2MP) dengan nilai sebesar 12,475, jarak kamera (12 cm) dengan nilai sebesar 13,572 dan kontras (1,7) dengan nilai sebesar 2,785.
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Adobe Edge Animate CC For Dummies

Adobe Edge Animate CC For Dummies

To actually choose a color, you can click and drag the circle around the graph, or you can use the first slider to change the hue. The second slider affects how light or dark the color appears. The third slider affects transpar- ency, which is useful if you want to see other elements that might be placed under the element you are adding color to. Another option is to change the color by typing in specific RGBa, Hex, or HSLa color values in the text box. If you have an image or other element on the Stage that has a color you want to use, then you can use the Eyedropper tool to sample a color for use on a selected element. When you select the Eyedropper tool, you see a target with a large ring around it as shown in Figure 9-5. As the target passes over the Stage, the color it will sample is shown in the ring. When you pass over the color you like, simply right click and that color is automatically applied to the selected element. You can also save this color by pressing the + icon.
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Photoshop CS5 All in One For Dummies

Photoshop CS5 All in One For Dummies

The extent of the shift depends on the colors in the RGB image and how many of them are out of gamut. Photoshop replaces RGB colors that are out of gamut with the closest match available within the CMYK gamut, often replacing the electric blues, fiery reds, and sunny yellows with duller, mud- dier CMYK equivalents. Unfortunately, you can’t do anything to prevent this replacement. It’s just the way of the world of color. However, if you can select colors (instead of acquiring them from a scan), be sure that you don’t select any colors that are out of gamut to begin with. You can also soft proof colors (preview the effects of your CMYK conversion without actually con- verting) by choosing View ➪ Proof Setup ➪ Working CMYK. Check out Book II, Chapter 3 for details about selecting colors and soft proofing.
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Performance analysis of color matching technique for teeth classification based on color histogram

Performance analysis of color matching technique for teeth classification based on color histogram

The accuracy level of performance will influence the computation time, which is caused by the kind of algorithm used for calculation. In RGB color models, the NN achieves best performance for accuracy level. However, the NN takes long compu- tation time up to 2015 seconds. This condition is not suitable when this system is applied to the real hardware. The suitable algorithm is using KNN having smallest computation time of only 1 second. The advantage of KNN algorithm is still stable when the number of bins are increased, the computation time is still 1 second.
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