channel

Power Density Function amplitude of sinus function with random phase shift

Raadar detection aLGORITHM

对于IEC谐波检测的程序，采用hanning窗函数，然而求解(For the IEC harmonic detection process, using hanning window function, but the solution)

说明：  灰度图像的位面图程序 matlab环境下写的 希望能有帮助(Gray image Bit plane map program， hopes to help you)

C++ implementation of Local Self Similarity. shi xian tu xiang de rong

extremely useful GU code for engg

Test Template Class Source Code for Andriod.

AM mod and demod using matlab and simulink(AM mod and demod using matlab and AM mod and demod using matlab and )

power_transient.rar.功率电子领域matlab仿真文件，已经验证过，程序运行正常(power_transient.rar, Power electronics field matlab simulation file, has already been verified, the normal operating procedures)

A. Fusion at the Feature Extraction Level The data obtained from each sensor is used to compute a feature vector. As the features extracted from one biometric trait are independent of those extracted from the other, it is reasonable to concatenate the two vectors into a single new vector. The primary benefit of feature level fusion is the detection of correlated feature values generated by different feature extraction algorithms and, in the process, identifying a salient set of features that can improve recognition accuracy [14]. The new vector has a higher dimension and represents the identity of the person in a different hyperspace. Eliciting this feature set typically requires the use of dimensionality reduction/selection methods and, therefore, feature level fusion assumes the availability of a large number of training data.