categories. My research on Multiscale Data Thresholding is coming along.

I will start putting some intermediate results soon.

Some of the interesting books I read or am reading currently on math and engineering:

The Code Book by Simon Singh If you are interested in the history of encryption and codes this is a great book. Many interesting ideas about how to attack certain type of combinatorial problems without killing yourself with raw complexity.

Prime Obsession The story of Prime Numbers and the famous Riemann Hypothesis (Short description by Wolfram Research) There is also a proof which is not validated yet (Proof of Riemann Hypothesis)

Some links:

Statistical Process Control from NASA

What is PID (simple and short)?

Other Interesting Tuning Algorithms

Interactive online education site from McMaster

More on PID

- A good starting point for the terms: Evaluation Stocks http://stocks.about.com/od/evaluatingstocks/
- Collection of introductory articles on Stock Picking http://www.investopedia.com/university/stockpicking/
- Golden ratio and Fibonacci Numbers in Stock Market http://goldennumber.net/stocks.htm
- Data Mining Techniques: How to extract useful information from big amount of data: MIT Course on Datamining

Technology Review: Bridging the Genomic Divide

That small molecules have more chance to interact with proteins and that there are only very small number of them identified is amazing. Production of small molecules is much easier and thus many more of them can be tested in a combinatorial way. The article also mentiones that NIH is planning to make the results available to public and acedemia.

I remember that in high school I was thinking about using Bogazici channel (connects Black Sea to Marmara Sea, so it is salty water) and the earths magnetic field to generate electricity. If you calculate between the coasts of the channel there is a mV range difference in potential. If you have very large metal plates you can generate electricity. THe oposite works too : You pump electricity to those plates and with the help of the magnetic field of earth (perpendicular to earth) you can move the water.

about computers, software, technology and science in general to that

category.

It is about the Prisoner's Dilemma competition. The new winner is a set of cooperating agents. Very clever. The rules are so simple but yet the results are so interesting.

identification using multiscale thresholding. Check it out at my mathematics category.

.

A signal consists of multiple components occurring at different frequencies. Current analysis methods analyze signals either in time or frequency domain but not in both at the same time. Time series analysis is major topic in signal processing. Any control engineer spends endless nights studying the Fourier transformations which convert a time series into the frequency domain and you can draw fancy graphs and shape your frequency response of your controller. These methods work very nicely for linear systems, meaning if you can open a valve infinitely :).

I am interested in analyzing signals in both domains at the same time. This would enable the use of methodologies from both domains, like constrains and events in time, and data smoothing (multiband frequency filters). Wavelets provide a nice framework for this type of research.

I currently work on simple multiscale threshholding algorithms. The idea and underlying math is extremely simple:

- Convert the signal into multiscale domain using Haar wavelets (http://cnx.rice.edu/content/m10764/latest/)
- Threshold the difference components (i.e. if less then epsilon set it to zero)
- Reconstruct the signal

- Use a moving window for the threshholding algorithm to get multiple reconstructed values for the same data points
- Analyze these points for variance
- Establish an event detection algorithm

My thesis was based on Multiscale Modelling. The building blocks of this methodology are a type of filters called *Wavelets*. Using wavelets it is possible to transform a first order system to time-frequency domain. In this domain the model representation is not anymore the simple linear time line but a tree structure where both time line (horizontal) and scales (frequency bands - vertical) are represented.

Once the model is transformed into this new domain it is also possible to create algorithms to solve a QR Programming problem based on this first order system. Instead of solving the problem globally it is possible to solve a subset which is a close approximation. This fast solution represents fine resolution at the beginning of the time line and a coarser resolution in the future where uncertainity in most physical systems make finer calculations obsolote anyhow.

I am going to go into more details in the comments section of this post in the next couple days. I will formulate a sample case and demonstrate the solution algorithm. I hope to have some links ready at some point too. **Please send comments if you have any**.

UPDATE: What is QR? (click here for the source)

The quadratic programming problem involves minimization of a quadratic function subject to linear constraints. Most codes use the formulation

where is symmetric, and the index sets and specify the inequality and equality constraints, respectively.

The difficulty of solving the quadratic programming problem depends largely on the nature of the matrix *Q*.