mean-shift跟蹤算法中核窗口大小的自動選區
標簽： mean-shift 跟蹤算法 窗口 自動
上傳時間： 2015-12-15
上傳用戶：liuchee
This is not a very mean things.
上傳時間： 2015-12-17
上傳用戶：253189838
This is not a very mean things
上傳時間： 2015-12-17
上傳用戶：zhangyigenius
This is not a very mean things
上傳時間： 2013-12-21
上傳用戶：xuanjie
This is not a very mean things
上傳時間： 2013-12-27
上傳用戶：龍飛艇
This is not a very mean things
上傳時間： 2015-12-17
上傳用戶：米卡
This is not a very mean things
上傳時間： 2014-01-09
上傳用戶：sy_jiadeyi
We address the problem of blind carrier frequency-offset (CFO) estimation in quadrature amplitude modulation, phase-shift keying, and pulse amplitude modulation communications systems.We study the performance of a standard CFO estimate, which consists of first raising the received signal to the Mth power, where M is an integer depending on the type and size of the symbol constellation, and then applying the nonlinear least squares (NLLS) estimation approach. At low signal-to noise ratio (SNR), the NLLS method fails to provide an accurate CFO estimate because of the presence of outliers. In this letter, we derive an approximate closed-form expression for the outlier probability. This enables us to predict the mean-square error (MSE) on CFO estimation for all SNR values. For a given SNR, the new results also give insight into the minimum number of samples required in the CFO estimation procedure, in order to ensure that the MSE on estimation is not significantly affected by the outliers.
標簽： frequency-offset estimation quadrature amplitude
上傳時間： 2014-01-22
上傳用戶：牛布牛
How the K-mean Cluster work Step 1. Begin with a decision the value of k = number of clusters Step 2. Put any initial partition that classifies the data into k clusters. You may assign the training samples randomly, or systematically as the following: Take the first k training sample as single-element clusters Assign each of the remaining (N-k) training sample to the cluster with the nearest centroid. After each assignment, recomputed the centroid of the gaining cluster. Step 3 . Take each sample in sequence and compute its distance from the centroid of each of the clusters. If a sample is not currently in the cluster with the closest centroid, switch this sample to that cluster and update the centroid of the cluster gaining the new sample and the cluster losing the sample. Step 4 . Repeat step 3 until convergence is achieved, that is until a pass through the training sample causes no new assignments.
標簽： the decision clusters Cluster
上傳時間： 2013-12-21
上傳用戶：gxmm
簡要介紹mean-shift算法的原理，并且在圖像處理方面的應用
標簽： mean-shift 算法
上傳時間： 2014-01-07
上傳用戶：釣鰲牧馬
