1
1
1 
1
nameless tee Map Playtime (hours)
------------- ----------------
Multeasymap 12089
ctf5 5764
TeeTown 4198
Kobra 3663
Blmap4 2763
BlmapChill 2724
fng 2708
Copy Love Box 2370
Tutorial 1554
blmapV3ROYAL 1470
Grandma 1279
Kobra 2 1275
Sunny Side Up 1155
Kobra 4 1152
Sunny Land 1142
Category Playtime (hours)
---------- ----------------
Novice 31464
Brutal 6532
Moderate 5835
Solo 2056
Oldschool 1792
DDmaX.Next 1634
Insane 1389
Dummy 1080
DDmaX.Pro 696
DDmaX.Easy 238
Race 160
DDmaX.Nut 71
Fun 70
Total Playtime (hours)
----------------------
91502 
2polynomial/polynomial where the denominator has higher or equal degree to denominator(sI - A)^{-1} into adj(sI-A)/det(sI-A) which doesn't help much since I still don't have A alone anywhere 
(sI-A) either so that's not too big an issue 
H(s) for an LTI SISO system. How do you come up with the state space representation A, B, C, D The inspiration for the Fast Fourier Transform (FFT) algorithm can be traced back to the seminal work of mathematician Carl Friedrich Gauss, who developed the Discrete Fourier Transform (DFT) in the early 19th century. The DFT is a mathematical technique used to analyze the frequency content of a signal, and it involves performing a series of complex mathematical operations on the signal using Fourier series.
While the DFT was a powerful tool, it was computationally expensive and time-consuming, especially for large datasets. In the 1960s, Cooley and Tukey independently developed the FFT algorithm, which is a much faster and more efficient way of computing the DFT.
The key idea behind the FFT algorithm is to break the DFT calculation into smaller, simpler sub-problems, and then use recursion to solve these sub-problems in a more efficient way. By doing this, the FFT algorithm reduces the number of computations required to perform the DFT, making it much faster and more practical for real-world applications.
The FFT has since become a fundamental tool in many areas of science and engineering, including signal processing, image processing, and numerical analysis. Its development and widespread use have revolutionized the way we analyze and understand complex data sets, and it remains an active area of research and development today.The inspiration for the Fast Fourier Transform (FFT) algorithm can be traced back to the seminal work of mathematician Carl Friedrich Gauss, who developed the Discrete Fourier Transform (DFT) in the early 19th century. The DFT is a mathematical technique used to analyze the frequency content of a signal, and it involves performing a series of complex mathematical operations on the signal using Fourier series.
While the DFT was a powerful tool, it was computationally expensive and time-consuming, especially for large datasets. In the 1960s, Cooley and Tukey independently developed the FFT algorithm, which is a much faster and more efficient way of computing the DFT.
The key idea behind the FFT algorithm is to break the DFT calculation into smaller, simpler sub-problems, and then use recursion to solve these sub-problems in a more efficient way. By doing this, the FFT algorithm reduces the number of computations required to perform the DFT, making it much faster and more practical for real-world applications.
The FFT has since become a fundamental tool in many areas of science and engineering, including signal processing, image processing, and numerical analysis. Its development and widespread use have revolutionized the way we analyze and understand complex data sets, and it remains an active area of research and development today. 
