D169 Demo - DMA0 repeated Burst to-from RAM, Software Trigger
Description A 32 byte block from 220h-240h is transfered to 240h-260h
using DMA0 in a burst block using software DMAREQ trigger.
After each transfer, source, destination and DMA size are
reset to inital software setting because DMA transfer mode 5 is used.
P1.0 is toggled durring DMA transfer only for demonstration purposes.
** RAM location 0x220 - 0x260 used - always make sure no compiler conflict **
ACLK= n/a, MCLK= SMCLK= default DCO ~ 800k
OFDM是無人機高速遙測信道中的主要傳輸技術之一，但是OFDM系統的主要缺陷之一是具有較高的峰均比。文中研究了一種信道糾錯編碼與迭代限幅濾波算法（repeated Clipping and Filtering，RCF）相結合的峰均比抑制方案。仿真結果表明，RCF算法能夠實現峰均比的有效抑制，卷積編碼和Turbo編碼能夠有效抑制RCF算法產生的限幅噪聲，降低系統誤碼率。
PID calculations for the _pid structure *a. This function uses the positional form of the pid equation, and incorporates an integral windup prevention algorithim. Rectangular integration is used, so this function must be repeated on a consistent time basis for accurate control.
Ink Blotting
One method for escaping from a maze is via ‘ink-blotting’. In this method your starting square
is marked with the number ‘1’. All free, valid squares north, south, east and west around the
number ‘1‘ are marked with a number ‘2’. In the next step, all free, valid squares around the two
are marked with a ‘3’ and the process is repeated iteratively until :
The exit is found (a free square other than the starting position is reached on the very edge
of the maze), or,
No more free squares are available, and hence no exit is possible.
Playfair Cipher
1.not even the large number of keys in a monoalphabetic cipher provides security
2.one approach to improving security was to encrypt multiple letters
3.the Playfair Cipher is an example
4.invented by Charles Wheatstone in 1854,but named after his friend Baron Playfair
Playfair Key Matrix
1.a 5X5 matrix of letters based on a keyword
2.fill in letters of keyword (sans duplicates)
3.fill rest of matrix with other letters
Encrypting and Decrypting
-plaintext is encrypted two letters at a time
1. if a pair is a repeated letter, insert filler like X’
2. if both letters fall in the same row, replace each with letter to right (wrapping back to start from end)
3. if both letters fall in the same column, replace each with the letter below it (again wrapping to top from
bottom)
4. otherwise each letter is replaced by the letter in the same row and in the column of the other letter of the pair
Cooperation has been the subject of intensive study in the social and biological
sciences, as well as in mathematics and artificial intelligence. The fundamental
finding is that even egoists can sustain cooperation provided the structure of
their environment allows for repeated interactions (Axelrod 1984).
The serious study of the practice of how to determine the appropriate content of a
specification is a seldom-appreciated pastime. Those who have the responsibility to
design a product would prefer a greater degree of freedom than permitted by the con-
tent of a specification. Many of those who would manage those who would design
a product would prefer to allocate all of the project funding and schedule to what
they consider more productive labor. These are the attitudes, of course, that doom a
project to defeat but they are hard to counter no matter how many times repeated by
design engineers and managers. A system engineer who has survived a few of these
experiences over a long career may retire and forget the past but we have an endur-
ing obligation to work toward changing these attitudes while trying to offer younger
system engineers a pathway toward a more sure success in requirements analysis and
specification publishing.
The past decade has seen an explosion of machine learning research and appli-
cations; especially, deep learning methods have enabled key advances in many
applicationdomains,suchas computervision,speechprocessing,andgameplaying.
However, the performance of many machine learning methods is very sensitive
to a plethora of design decisions, which constitutes a considerable barrier for
new users. This is particularly true in the booming field of deep learning, where
human engineers need to select the right neural architectures, training procedures,
regularization methods, and hyperparameters of all of these components in order to
make their networks do what they are supposed to do with sufficient performance.
This process has to be repeated for every application. Even experts are often left
with tedious episodes of trial and error until they identify a good set of choices for
a particular dataset.
