Brewster's angle

Recently I've watched several videos about Brewster's angle and quantum mechanics for localizing sound. I believe that by measuring the angle of refraction we can learn about the originating paths of sound waves.

Limits of perceiving images through light can be remedied in acousting modeling

The perception of images through light excels at perspective geometry which is an abstract, generalized simplification form of geometry. This is in contrast to projective geometry which is 3D. The difference between the two is projective geometry it is not possible to talk about angles as in a Euclidean space such as perception geometry. A good tool to get familiar with the topics discussed in this blog is to play with geogebra. All objects that have energy produce sounds of their own. Perhaps these sounds are so constant that they are impossible for a human to perceive them. However watchalerts uses moving objects so we can measure redshift. By using a wave slower than light, such as florescence and also sound, it makes a more accurate image. The speed of sound in air is 340 meters abd moving through a slower medium it is accelerated by a factor of four. The speed of light in air is 300 million meters per second. The velocity of a wave as a single wave propogation frequency is called a phase. A phase retractive index is how much slower the speed of light is traveling as relative to the speed of light. Augmented reality algorithms developed by Qualcomm Vuforia takes multiple pictures and finds points of commonality between them. Acoustics is capable of noticing sounds and finding commonalities between them similar to Vuforia. This is known as homography Vuforia then draws an image based on mathematical principles using a codebook which is a list of reconstruction levels such as distict object edges, colors such as gradients and other features. Modeling human perception, these pay attention to the destinction between an object and scene perception. Also like human perception the reconstruction uses top down feature finding. I am looking at the possiblity of using a feature-gate, which uses top down and bottom up feature finding. These rely on visual saliency to determine features. Some features consist of RGB color, color saturation, and orientation (edge and symmetry), contrast, foreground, background. These filter dictionaries are capable of deriving multi-images. Sound is very much like light as described by quantum physics. Like light, interference is coming from a number of different sources and due to scatter. We perceive based on how the wave forms interact with the object characteristics about the object.

I've also looked at AAC audio encoding, they do compression through finding distortions through overlaying frames slightly over the preceeding and succeeding frames to make sure the energy is consistant. In our case we depend on automatic color equalization and using mean square error and peak signal to noise. Once detected, we calculate the wavelength of the noisy signal and modify it so it is within range. I am looking at doing degaussing.

There is much work done with OpenCV and ARToolkit to detect people as regions of interest. Despite the fact that as of 2014 human face detection by computers outperformed humans (as described in the book Data and Goliath), even the best well known cloud computing platforms are not perfect. Facebook has difficulty with thresholds of image detection. The paper Image Analysis and Understanding Techniques for Breast Cancer Detection from Digital Mammograms discusses thresholds when abnormalties are present. They do pre-processing through histograms, Then they do segmentation through such methods as connectivity and compactness, regularity and boundaries, homogeneity in terms of color and texture, and differentiation through neighboring regions. Some of these methods involve region growing, split and merge, k-means clustering, watershed technique. Should this fail there are adaptive thresholding methods such as histogram shape-based methods including Otsu's method which automatically performs shape based threshold to reduce the image to a binary image, clustering-based methods which include fuzzy c-means and k-means, entropy based methods which consist of contrast, energy, and correlation or how a measure of a linked picture relates to the image as a whole, and spatial.

Some projects I'm currently looking at:
Neuromorphic Vision Toolkit (Produced by University of Southern California)
ARToolkit (authored by the University of Washington)
Kinovea (a camera driven app much like ubersense for sports. Although I found some things we can use I like the idea of working with a codebook described by XML)
ipsy (a webcam platform used in combindation with OpenCV. This prodives nice interfaces for working with SVG and has different device drivers for attaching to Windows)
Vuforia uses the Unity 3D and visual studio in C#.

One nice thing about using these projects (Kinovea, ISpy, Vuforia, Unity 3D) is that they all have C# interfaces. The Blender project involves python and KNIME works with Java. This does not mean that I would hesitate to use these projects. Due to the size of the C# tools, I would reflect them to ironpython and from then to python and use Kivy to make them work cross platform.

Works Cited:
"Acoustic Camera." Wikipedia. Wikimedia Foundation, n.d. Web. 08 June 2015.

"Advanced Audio Coding." Wikipedia. Wikimedia Foundation, n.d. Web. 08 June 2015.

"CEM Lectures." YouTube. YouTube, n.d. Web. 08 June 2015.

"Compression Artifact." Wikipedia. Wikimedia Foundation, n.d. Web. 08 June 2015.

"Eric Betzig Plenary Presentation: Single Molecules, Cells, and Super-resolution Optics." YouTube. YouTube, n.d. Web. 08 June 2015.

"Inexpensive 'nano-camera' Can Operate at the Speed of Light." MIT News. N.p., n.d. Web. 08 June 2015.

"Keynote: Preservation and Exhibition of Historical 3D Movies [9011-83] [SD&A 2014]." YouTube. YouTube, n.d. Web. 08 June 2015.

"Korotkoff Sounds." Wikipedia. Wikimedia Foundation, n.d. Web. 08 June 2015.

"Lecture 22 (EM21) -- Slow Waves." YouTube. YouTube, n.d. Web. 08 June 2015.

McCormick, Douglas. "A "Sound Camera" Zeroes In on Buzz, Squeak, and Rattle." N.p., n.d. Web. 08 June 2015.

"Microscopy: Super-Resolution: Overview and Stimulated Emission Depletion (STED) (Stefan Hell)." YouTube. YouTube, n.d. Web. 08 June 2015.

"Projective Geometry." Wikipedia. Wikimedia Foundation, n.d. Web. 08 June 2015.

"Q & A: Speed of Sound and Light." Q & A: Speed of Sound and Light. N.p., n.d. Web. 08 June 2015.

"Rectification of an Oblique Image." 8 June 2015. Speech.

"Short-time Fourier Transform." Wikipedia. Wikimedia Foundation, n.d. Web. 08 June 2015.

Southwall, Richard. "Projective Geometry 1 Without Equations, Conics & Spirals." 8 June 2015.

Srivastava, Rajeev, S. K. Singh, and K. K. Shukla. Research Developments in Computer Vision and Image Processing: Methodologies and Applications. N.p.: n.p., n.d. Print.

"Vuforia Tutorial: Qualcomm's Augmented Reality SDK." YouTube. YouTube, n.d. Web. 08 June 2015.

stemulate

I am working on a new approach to classify data in images. I have studied different file types for GIS data and the nice thing about geological data is a lot of it is quantified. Roads are given names, distances are measured, and altitude is measured. I recently looked at open street maps and they say as of May 2015 there has been a number of attempts to combine XML/XSLT/ and SVG however they are in early stages of development. The official  OSM file for the geographic system for the earth is 150 GB which is huge. Microsoft research in 25 February 2015 did an expiremental project called image composite editor which was able to understand images they were able to do image completion. I want to find a way of painting an image using SVG and find the parts that move and describe this as delta data. In the 1990's the approach was to do these changes so fast that a human cannot detect when the screen was refreshed. However when a CRT monitor appears in video these changes are augmented. Today through digital technology we have come closer to high resolution videography. However we know through quantum physics there are jumps the brain is unable to detect. we as humans are trained not to notice these differences. This is described in the psychology text book The Invisible Gorilla. The book the organized mind explains that the human mind works with a limited bandwidth. For more detail please see my paper "Jurassic extrapolation increases accuracy of speech to speech engine". by using composite images using delta files only painting changed elements I hope to create delta transform files. By using a technology similar to breaking apart elements of a GIS file I hope to use a dirative of SVG based on XML and XSLT to make composite images which can be animated. The benefit of using SVG based technology is that it is not based on pixels making it run with media queries to be rapidly rendered on any size of screen. Unfortunatly as of May 2015 it is not true that a SVG would be smaller than a raster image. Much research was given to fail gracefully on older browsers with HTML5 which is a XML deravitive. I hope to use a new format of SVG where it defines what the person is looking at such as a lake. It then uses math to determine what the lake looks like such as depth of the water and clouds that are in the image to determine shadows. I have a test project which is to make a flight simulator using images that exist in GIS data such as World Wind and open street maps that would render fast enough to provide detailed images of the scenery while the open source Flight Gear provides the simulator of the planes. This is a challenge because Flight Gear uses C and C++ to make their tools. One approach that I might make to store the data so they can be quickly queried is MongoDB which is more commonly used with python and java. Each of these languages are supported on android and kivy makes it possible to make cross platform apps. key challenges that I am attempting to solve at this point are:
size of file speed of scenery rendering needs to coorelate to speed of simulated aircraft.
threshhold of image changes for delta streams needs a threshhold of perceived changes needs to be within the perception range of human cognition. this can be measured using the data analytics toolkit KNIME. Another beneficial toolkit is mathbuntu which includes SAGE mathmatics and geogeraph.They also have LYX which uses Donald Knuth's LATEX fomat serves as a basis for the SVG format.

Although the fight simulator would serve as a "toy" project for demonstrating this technology the aim is to  serve for the physical therapy platform which would help everyone eventually as we each would benefit from physical therapy at some point in our lives.

what is watch alerts

Watch alerts is an artifical intelligence expert system intended to aid physical therapists find points of balance for weakness and analyzes how a person carries themselves as compared to other people. It uses such features as computer vision, accelerometer, and gyroscope. It differs from competing programs because everything that is required is open source or included on the mobile platform. The following are worth checking
out

wilcoxon statistical modeling
remote sensing microphone (spectroscopy)
hyrax (mobile hadoop) looking for a self-contained platform that utalizes android's spec