Posts

Color Electrophoretic Display

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Until now, the only way to get "full" color from an EPD -- at least the only way that E Ink has shown us -- is placing a matrix color filter in front of the monochrome display. E Ink showed the way forward a few years ago with a black, white, and red display, which managed to control particles of three different colors using differences in mobility and a cleverly designed controlling waveform. At Display Week 2016, E Ink introduced an impressive expansion of this approach, in which particles of four different colors are included within each microcapsule, given different mobilities through different sizing, and driven with a pulsed controlling wave movement that permits the creation of thousands of colors, as explained by E Ink's Giovanni Mancini.     The resulting display showed impressively bright and saturated colors and drew crowds.  When a new image was written, the display would flash several times.  It took about 10 seconds for a new image to build...

Electrophoretic Display

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Electrophoresis is the motion of dispersed particles relative to a fluid under the influence of a spatially uniform electric field. E Ink Corporation further describes their electronic ink technology as a type of display made of millions of tiny and transparent microcapsules—measuring about the diameter of a human hair. Each microcapsule represents a pixel and each contains a positively charged white pigment particle and a negatively charged black pigment particle. Upon the application of positive or negative electric, corresponding particles move to the top of the microcapsule where they become visible to the user. This makes the surface appear white or black at that spot. Advantages: 1) Lower Power Consumption: Note that this display technology is a bistable display. This means that the image on an electrophoretic display screen is retained even when all power sources are removed. The display is only consuming power whenever the displayed image changes. 2) Refl...

Histogram Equalization Algorithm Implementation Using MATLAB

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  Histogram equalization is a method in image processing of contrast adjustment using the image's histogram. The histogram of a digital image is a distribution of its discrete intensity levels in the range [0,L-1]. The distribution is a discrete function h associating to each intensity level: r k the number of pixel with this intensity: n k . Normalization of a Histogram Normalize an histogram is a technique consisting into transforming the discrete distribution of intensities into a discrete distribution of probabilities. To do so, we need to divide each value of the histogram by the number of pixel. Because a digital image is a discrete set of values that could be seen as a matrix and it's equivalent to divide each n k by the dimension of the array which is the product of the width by the length of the image.   Equalization of a Histogram Histogram equalization is a method to process images in order to adjust the contrast of an image by modifyin...

Collision Avoidance Bot

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This project is about mobile robot simulation for avoiding collision in a given environment. STEP1 Step1 is exporting a CAD model of PIONEER-3DX to .xml file and using the simulink file thus created in matlab. STEP2 The simulink file thus created will have constraints on wheels for robot motion By changing the constraints on the robot, we can decide in which direction to move the robot. If the constraint on left_wheel=1 and that on right_wheel=0, it will turn in right direction and vice versa. To make the robot move forward or backward, both the constraints should be equal. STEP3 Step3 involves making the enviroment and inlining the enviroment with the robot file. This file is uploaded in vr sink block of the file which is the last block of the code.  STEP4 Step4 is about creating the control logic. The control logic depends on a previously defined look-up table. The control logic is a stateflow that takes sensors as input and...

Feature Detection And Matching

How Outliers are Removed?  The matched features between the images have a euclidean distance. Features with euclidean distance beyond the average euclidean or a specified threshold are removed as outliers. SIFT(Lowe, 2004): Detects special features regardless of the scale and orientation of the image, and allows you to reliably detect the same special features even in slightly distorted images, adding noise or changing the lighting and / or viewing point. SIFT detects potentially specific features, and measures the stability of these properties and determines their magnitude by eliminating unstable properties. Then, according to the local gradient direction, for each specific feature, one or more orientations are calculated and assigned. With this information, image data properties can be normalized to scale, position and orientation - so the properties become scalable with respect to these transformations. The method also includes a descriptor, which detects the special pro...

Bitwise Manipulation Using C++

/*Write a function to determine the number of bits you would need to flip to convert from integer A to integer B Input : 29 (11101) & 15 (01111) Output: 2*/ # include<iostream> using namespace std; int main() {     int a,b,c[5],d[5],count,i, bits;     a=29;     b=15;     count=0;   bits=0;         while((a>0) || (b>0))     {     c[count]=a%2;     d[count]=b%2;       a=a/2;     b=b/2;     count=count+1;       if (((a==0) || (a==1)) && ((b==0) || (b==1)))       {         c[count]=a;         d[count]=b;         break;       }        ...