Archive for the 'Analysis and production' Category Page 3 of 12



Mar 24

Biosensors a quick overview

Biosensors is one of the fast growing area today. This technology has a huge potential that can be compared to RFID. It is likely that they will be everywhere in a couple of years. Surprisingly however they seem far less popular, at least in my circle, than RFID. So after seeing a very impressive talk about them on thursday, I wanted to share my enthusiam about them with you.

What is a biosensor ?

A biosensors is defined in scientific journal as:

A biosensor is an analytical device which converts a biological response into an electrical signal. The term ‘biosensor’ is often used to cover sensor devices used in order to determine the concentration of substances and other parameters of biological interest even where they do not utilise a biological system directly. ”

A more simple definition would be that a biosensor is a sensor that is able to tell you what biological components are present in a material such as water or blood.

Why biosensor will change my life ?

It may be not obvious how biosensors are usefull at first sight so let me give you three domains of application:

Fighting Bioterrorism

That is the most obvious use of biosensors, helping to fight bioterrorism. For instance the MIT Lincon Lab have developed a powerful sensor that can detect airborne pathogens such as anthrax and smallpox in less than three minutes.

080304120746

This device, called PANTHER (for PAthogen Notification for THreatening Environmental Releases), could be used in buildings, subways and other public areas, and can currently detect 24 pathogens, including anthrax, plague, smallpox, tularemia and E. coli.

Water Drinking

Biosensors can be used as early warning screening tools for drinking water security because they can spot toxics in a matter of minutes. Hence they can be use to monitor critical locations of the water system, such as pump stations, or prior to the biological process in a waste water treatment plant.

Water

For example Toshiba has developped a prototype to detect harmful substances in groundwater to protect against environmental contamination :

img2103

Food safety

Imagine that in the middle of the night you get thirsty, so you go to the kitchen, grab the bottle of milk and you are ready to get a nice cup of milk. Fortunatly, you look at the biosensor and see that it has turn red. Thanks to it you know that something went wrong, and that you must not drink the milk because it now contains a potential biological substance that will make you sick. Thanks to the biosensor, you will not be sick for your important meeting/date.

milk

Instead of expiration date, biosensors will be able to tell us if the food is safe or not. This will be far more accurate and safe. Maybe we will have a tool like this on day:)

biosensor

Of course theses are just few examples of what biosensors are good for. They can also be used to diagnose drugs in blood, or in crime forensic.

 

Anatomy of a biosensor

A biosensor is composed of the following part

  • The biocatalyst that will react (complex) with the biological substance.
  • The transducer also called the detector element that will transform the reaction that occurs in the biocatalyst into a signal which more easily measurable
  • The amplifier used to make the transducer ouput signal more powerfull so it can be treated by a chips/computer
  • The signal processing engine that will interpret the sensor signal a give it a meaning
  • The Gui that will display the result

biosensor-diagram

Conclusion

If biosensors were first developped for glucose monitoring in diabetes patients since a few years the market has exploded due to the wide range of possible application. Currently, biosensors represent a rapidly expanding field, at the present time, with an estimated 60% annual growth rate.

Feb 25

Comparing photo Magnification algorithms used in photo software

If you have ever watched NCIS, CSI, numb3rs or any modern police related TV show you probably have seen them take a digital photo and enlarge it to reveal details. Because it is a TV show the photo magnification work perfectly and show wonderful details. Well this is fiction and in real world this is not going to append. However over the years, magnification algorithms have made serious progress so I thought it could be interesting to see how everyday softwares handle magnification.

The problem

The problem with photo magnification is the following: the computer has to infer information that does not exist. A photo as you probably already know is made of pixels (elementary points), hence an upscaled photo has more pixels that the original one. The question is which color theses new pixels should have ? Since there were not in the original photo, you have to guess. That exactly what does a magnification algorithm: It tries to guess what the new pixels should look like according to the original image. That is why TV show techniques are fiction: you can’t find information that wasn’t in the original set of pixels.
I have to precise that in this post I am talking about photo magnification, also know as interpolation, not video upscaling. Video interpolation algorithms can in some case combine multiples frames of the video to have a bigger pictures. (I save this for a future post).

How magnification work ?

The basic idea behind every magnification (interpolation) algorithm is to guess the value of the new pixel according to the real pixels that you find around it. To make it more efficient, many techniques have been tried such as bicubic, Lanczos, XinLin, DDL ones . If you want learn how an interpolation algorithm works read this excellent tutorial made by Sean. If you just want the basic idea, take a look at the diagram below taken from this tutorial:

Objectives

There is a bunch of comparison between extrapolation algorithm in research publication. This post is a little different because it has a pragmatic approach of the problem. It seek to answer the following question:

What result can I expect from my everyday software on a standard photo”

If you want an analysis of high end research algorithms that is all about numbers and variations take a look at this page.

Software tested

I choose three widely used photo software and a research soft wares to compare their results on average photos:

  1. Windows paint as baseline
  2. Gimp 2.4.4 which is the open source reference
  3. Photo shop CS3 which is the commercial reference
  4. SAR 3.4 which implement blending edge research algorithm

 

Methodolgy

In order to make the test the most objective possible, I did the following:

  • I took three photos with a resolution equal or above 400×400.
  • I downscale them to 200×200.
  • I upscale them to 400×400 using the different soft wares.
  • I made a visual comparison of each algorithm result for a relevant part of the photo.

Every photos during the process have been saved using the PNG algorithm to ensure that no distortion is introduced by a compression algorithm. Complete results and raw material are provided so you can judge by yourself, do the same, and even made more tests (I you do, I would be happy to publish them). Two of the three photos are human portrait because face are known to be hard to upscale and this is of course a very popular topic :) Please note that because the test was made on three images, this post cannot be considered as a reference benchmark (The methodology is correct but there is simply not enough data).

The photos

Photo 1 Evangeline Lilly aka Kate

The first photo is a portrait of Evangeline Lilly which play Kate in Lost. Here is the original (400×400) photo:

evangeline

The resulting downscaled photo (200×200)

evangeline

Photo 2 Matthew Fox aka Jake

I used a photo of Matthew Fox as the second photo, because unlike Evangeline, he have short hairs which should be easier to upscale. The original photo (400×400):

matthew-fox

The resulting downscaled photo (200×200)

matthew-fox

Photo 3 Nightwish Dark Passion play cover

As the last photo, I used the cover of the album Dark passion play because I wanted to see how upscaling algorithm handle light diffusion and text. The original photo (400×400):

NightwishDarkPassionPlay

The resulting downscaled photo (200×200)

NightwishDarkPassionPlay

The results

For each software, I tried various interpolation algorithms if possible. For the visual comparison I kept, the one that seems to be the better for each software. On the right of the comparison, you have the expected photo which is directly taken from the original file. Of course no algorithm is expected to be that perfect, but it allow to put things in perspective. In the visual comparison, I kept the recommended resize algorithm for Photo shop CS, the most advanced method (sic) for Gimp, and the back projected Jensen DDL for SAR. Complete results of extrapolation algorithm are visible in the photo galleries.

Result for Evangeline

compare-eva

As expected the SAR algorithm is clearly better. What is unexpected it that the paint algorithm is not so bad. You can found all the interpolations results below

Result for Matthew

compare-mat

Here the logic is respected, and there is a clear difference of quality, this is particular visible for the shirt and around the eyes. . You can found all the interpolations results below

Result for Nightwish

compare-night

Once again SAR is better, and this time the paint algorithm is clearly behind. This is obvious when you look at the word play. . You can found all the interpolations results below

Conclusion

As expected, the quality of the magnification is dependent of the software you use. Photo shop and Gimp are pretty close and SAR is way above. Note that SAR algorithms are way more slower than photoshop and GIMP algorithms because they performs a significant amount of computation. SAR is also much harder to use than the other products. If you dont understand the algorithms, it can give you hard time. If you want to see more comparison, AmericasWonderlands have made a very neat post about it. Feel free to send me your interpolation results so we will have a more significant corpus.