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Jul 13, 2015 · Convert PDF to Word Using C# PdfBox: This third-party Nuget package will be used to read a PDF file. DocX: This package will be used to write a Word document.

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can be programmed to require 3 n2 m + 3 nm2 + nm + m3 + 1 m2 + 1 m 2 2 2 2 FLOP s Alternatively, when R is a diagonal matrix, the measurements can be equivalently treated as m sequential measurements with a zero-width time-interval between measurements, which results in signi cant computational savings

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Brett Steenbarger, PhD, is clinical associate professor at SUNY Upstate Medical University and a coach of traders in proprietary firm, hedge fund, and bank settings He has authored several books on trading psychology, including The Daily Trading Coach: 101 Lessons for Becoming Your Own Trading Psychologist (Wiley, 2009), as well as the TraderFeed blog (wwwtraderfeedblogspotcom)

CHAPTER 5 OPTIMAL STATE ESTIMATION At time t = kT , de ne P1 = (k) P H1 H= Hm x1 = (k) x R1 R= 0 0

.

(575)

Then, the equivalent scalar measurement processing algorithm is, for i = 1 to m, Ki xi+1 Pi+1 Pi Hi Ri + Hi Pi Hi = xi + Ki ( i Hi xi ) y = = (I Ki Hi ) Pi , (576) (577) (578)

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28 May 2018 ... An useful C# code snippet to convert PDF files into TXT plain-text or HTML in C# ... NET through a Java-port which is still called iTextSharp .

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Masters of the Universe: Long-Term Capital Management If you ve made a series of good trades and feel intoxicated with overconfidence, read this account of the rise and fall of Long-Term Capital Management (LTCM), the formerly profitable hedge fund managed by some of the smartest people in the country Before it collapsed, it was considered the most impressive hedge fund in history Many of LTCM s managers had earned numerous degrees from some of the best schools in the country At least two were Nobel Prize winners, and many were on the boards of the most prestigious brokerage firms The people running LTCM were considered geniuses, and reading about their downfall teaches important lessons about trading stocks Let s start at the beginning At first, LTCM was incredibly successful, earning as much as 40 percent per year for its wealthy clients At its height, it had over $100 billion in assets, much of it leveraged, including $6 billion in cash As it made more money, its arrogance grew People at LTCM were the masters of the universe, and in their minds, nothing could ever bring them down John Meriwether, their secretive but brilliant founder, handpicked a group of geeks who created complex mathematical computer models that traded bond derivatives As their profits grew, they made bigger and bigger fixed-income bets using sophisticated trading strategies such as bond and merger arbitrage Their complex models helped them to manage risk, or so they thought Unfortunately, they failed to program one scenario: a once-in-a-million doomsday situation in which a huge country like Russia could default on its currency, the ruble LTCM believed its computers would be able to forecast such a calamitous event Meanwhile, every year LTCM s partners wrote letters to their clients, which included how much they could theoretically lose; for example, 5 percent of their portfolio (the numerical odds, the partners wrote: no more than 12 times over the next 100 years,.

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with the state and error covariance matrix posterior to the set of measurements de ned by x+ (k) P+ (k) = xm+1 = Pm+1 (579) (580)

.

The total number of computations for the m scalar measurement updates is m 3 n2 + 5 n plus m scalar divisions Thus it can be seen that m scalar 2 2 updates are computationally cheaper for all m At the completion of the m scalar measurements x+ (k) and P+ (k) will be identical to the values that would have been computed by the corresponding vector measurement update The state Kalman gain vectors Ki corresponding to the scalar updates are not equal to the columns of the state feedback gain matrix that would result from the corresponding vector update This is due to the di erent ordering of the updates a ecting the error covariance matrix Pi at the intermediate steps during the scalar updates Example 56 To illustrate the di erences between the vector and scalar measurement updates, consider one measurement update for a second-order system having P (k) = H= 100 0 0 , x (k) = 0 100 0 10 00 1024 2120 00 10 , y= 07 03 1391 05 05 1484 , , and R = I4

56 IMPLEMENTATION ISSUES For the vector update, by eqn (572), the Kalman gain is K(k) = 06276 02139 02139 03752 02069 08136 00944 02999

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