特點(FEATURES) 精確度0.1%滿刻度 (Accuracy 0.1%F.S.) 可作各式數學演算式功能如:A+B/A-B/AxB/A/B/A&B(Hi or Lo)/|A| (Math functioA+B/A-B/AxB/A/B/A&B(Hi&Lo)/|A|/etc.....) 16 BIT 類比輸出功能(16 bit DAC isolating analog output function) 輸入/輸出1/輸出2絕緣耐壓2仟伏特/1分鐘(Dielectric strength 2KVac/1min. (input/output1/output2/power)) 寬范圍交直流兩用電源設計(Wide input range for auxiliary power) 尺寸小,穩定性高(Dimension small and High stability)
The LPC2292/2294 microcontrollers are based on a 16/32-bit ARM7TDMI-S CPU with real-time emulation and embedded trace support, together with 256 kB of embedded high-speed flash memory. A 128-bit wide memory interface and a unique accelerator architecture enable 32-bit code execution at the maximum clock rate. For critical code size applications, the alternative 16-bit Thumb mode reduces code by more than 30 pct with minimal performance penalty.
With their 144-pin package, low power consumption, various 32-bit timers, 8-channel 10-bit ADC, 2/4 (LPC2294) advanced CAN channels, PWM channels and up to nine external interrupt pins these microcontrollers are particularly suitable for automotive and industrial control applications as well as medical systems and fault-tolerant maintenance buses. The number of available fast GPIOs ranges from 76 (with external memory) through 112 (single-chip). With a wide range of additional serial communications interfaces, they are also suited for communication gateways and protocol converters as well as many other general-purpose applications.
Remark: Throughout the data sheet, the term LPC2292/2294 will apply to devices with and without the /00 or /01 suffix. The suffixes /00 and /01 will be used to differentiate from other devices only when necessary.
Description: FASBIR(Filtered Attribute Subspace based Bagging with Injected Randomness) is a variant of Bagging algorithm, whose purpose is to improve accuracy of local learners, such as kNN, through multi-model perturbing ensemble.
Reference: Z.-H. Zhou and Y. Yu. Ensembling local learners through multimodal perturbation. IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics, 2005, vol.35, no.4, pp.725-735.
Description: S-ISOMAP is a manifold learning algorithm, which is a supervised variant of ISOMAP.
Reference: X. Geng, D.-C. Zhan, and Z.-H. Zhou. Supervised nonlinear dimensionality reduction for visualization and classification. IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics, 2005, vol.35, no.6, pp.1098-1107.
The government of a small but important country has decided that the alphabet needs to be streamlined and reordered. Uppercase letters will be eliminated. They will issue a royal decree in the form of a String of B and A characters. The first character in the decree specifies whether a must come ( B )Before b in the new alphabet or ( A )After b . The second character determines the relative placement of b and c , etc. So, for example, "BAA" means that a must come Before b , b must come After c , and c must come After d .
Any letters beyond these requirements are to be excluded, so if the decree specifies k comparisons then the new alphabet will contain the first k+1 lowercase letters of the current alphabet.
Create a class Alphabet that contains the method choices that takes the decree as input and returns the number of possible new alphabets that conform to the decree. If more than 1,000,000,000 are possible, return -1.
Definition
Use
the fast Fourier transform function fft to analyse following signal. Plot the original signal, and the magnitude of its
spectrum linearly and logarithmically. Apply Hamming window to reduce the
leakage.
.
The hamming window can be coded in Matlab as
for n=1:N
hamming(n)=0.54+0.46*cos((2*n-N+1)*pi/N);
end;
where
N is the data length in the FFT.
