biaomianji

fsk modem simulation

基于RSSI的定位。本人精心编出来的哦。。()

数据处理的一种方法，可实现目标轨迹的其实确定，实验结果证明效果很好。(A method of data processing can be realized in fact a target track, experimental results show a good effect.)

数据挖掘作业 matlab实现DBSCAN算法 西电(数据挖掘作业 DBSCAN 西电)

说明：  利用目标以前的坐标点完成目标未来未知位置的预测，分别利用一个到五个点来进行预测(Using the coordinates of points before the completion of target objectives of the forecast future unknown location, respectively, using 1-5 point forecast)

gallstone segmentation and classification by kumares

％96年中国大学生数学建模竞赛B题:洗衣机(96% of university students in China Mathematical Contest in Modeling B theme : Washing Machine)

QPSK高斯噪声信道下的星座点和比特误码率仿真曲线(QPSK constellations point under gaussian noise channel and the bit error rate curve simulation)

利用matlab7进行系统建模与仿真的例子(Modeling and Simulation using matlab7)

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.