Monday, December 21, 2020

Computational Imaging and Microscopy

This is an excellent talk, and takes one of the most complex subjects and breaks it down simply from the beginning.  

     14:38 in to the video. 

  It took be the better part of 25 years to learn the secret of zooming in to a license plate from an impossibly Zoomed image.  Something that when shown in Sci-Fi TV shows in the 70's and 80's - 90's...  I just assumed was Bullshit.  

 I eventually learned the secret from one of the top digital imaging experts that's I've known 25 years, and well after his retirement.  He implemented it on super top secret hardware for satellite imaging, when I was still in grade school.  It used the fact that every pixel in your image represents a sine cardinal from the whole image.  Also known as Sinc function it is the continuous inverse Fourier transform of a rectangular pulse and can be thought of a Gaussian modulated sine wave, although I am not really sure if these are the exact equivalent though I see it implemented in RF applications like this. 
In his case the Lens had to be extremely well understood, and in the end it generated convolution filters that were able to be ran quickly and efficiently. 

What is interesting is to see this generalized in to a generic computational imaging problem.  It may actually yield better results with more information, but most likely will be much more computation. 

Computational imaging involves the joint design of imaging system hardware and software, optimizing across the entire pipeline from acquisition to reconstruction. This talk will describe new methods for computational microscopy with coded illumination, based on a simple and inexpensive hardware modification of a commercial microscope. Traditionally, one must trade field-of-view for resolution; with our methods we can have both, resulting in Gigapixel-scale images with resolution beyond the diffraction limit of the system. Our reconstruction algorithms are based on large-scale nonlinear non-convex optimization procedures for phase retrieval. Laura Waller leads the Computational Imaging Lab, which develops new methods for optical imaging, with optics and computational algorithms designed jointly. She holds the Ted Van Duzer Endowed Professorship and is a Senior Fellow at the Berkeley Institute of Data Science (BIDS), with affiliations in Bioengineering and Applied Sciences & Technology. Laura was a Postdoctoral Researcher and Lecturer of Physics at Princeton University from 2010-2012 and received BS, MEng and PhD degrees from MIT in 2014, 2015 and 2010, respectively. She is a Moore Foundation Data-Driven Investigator, Bakar fellow, Distinguished Graduate Student Mentoring awardee, NSF CAREER awardee and Packard Fellow.

Tuesday, December 15, 2020

Transmit a video stream to a PAL analog TV using low-frequency PWM


Uses STM32F411 a slow 6.86MHz PWM output to generate modulated transmitting output and the 9th harmonic is 61.71MHz (it is picked up on channel 3 of the TV).

He used an AVR ATTiny85 to generate PWM waveforms which were picked up by his TV.

Thursday, December 10, 2020

A camera that can look inside the keyhole to read the keys pattern!!

Currently $345 USD

The LockTech LTKS KwikSet Decoder is a WIFI enabled digital scope that when used with a compatible IOS or Android Smartphone makes decoding these locks ridiculously easy and fast!

- Decodes all current SmartKey locks (GEN 1, 2, 3, & 4) and SmartKey Control Key cylinders as well.
- A real glass mirror for the clearest image possible.
- Internal LED eliminates glare off the front of the lock.
- Position Alignment Spacers eliminate the guesswork of where you're looking at in the lock and locating individual wafers/pins during the decoding process.
- LED dimmer allows the user to increase or decrease the brightness inside of the lock.
- Live Video Display Feed, SnapShot Mode, or Video Mode.
- Rechargeable battery
- Magnetic Protective Storage Cap
- Spacers, Protective Cap, and Laminated Depth Chart are tethered for convenience.

 System requirement:

Android 4.2 and iOS 8.0 or later