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[matlab] code DL : 0: 自适应滤波的MATLAB实现（包括LMS，LRS、NLMS等算法-Adaptive Filtering MATLAB implementation (including the LMS, LRS, NLMS algorithm, etc.
Update : 2025-02-19 Size : 2kb Publisher : 李生
[matlab] LRS DL : 0: 进行时域分析的好程序，刚和师兄编的，仅供参考-Good time domain analysis procedures, and senior compiled just for reference only
Update : 2025-02-19 Size : 32kb Publisher : 方煜
[matlab] program DL : 0: 输入信号为正弦波加白噪声，分别用lms，lrs，维纳算法，滤出白噪声中的正弦波。-The lms，lrs filter。
Update : 2025-02-19 Size : 1kb Publisher : rindarabc
[Other] LRS DL : 0: 模式识别中的 P+M L-R算法，主要用来做特征提取-Pattern Recognition P+ M LR algorithm, mainly used for feature extraction
Update : 2025-02-19 Size : 1kb Publisher : MRlee
[Special Effects] reg-03-miccai03-intensity DL : 0: 医学图像处理，医学图像2D_3D配准，基于vc++开发。-(¨a© ?« ?¬ ,-‰ ® f« dˉ$° m jTonm?ƒ „ m?tYlhvft?mp±2mp_plnrs ‚ m rst`lnmptuƒ „ r3l‘ … `′￥}‰ v?ƒ „ m w$i?mplnaTb`wμe\b?ohonrszdr¶ wIonm?zf′ rsƒ ’ l„ o“ v?lnrsbft·bfeIv7juv?lnrsm?tYl?jLonm?bdj mWo“ v?lnrs ‚ m¸ g { lnb7r3lnƒ ?rstYl„ o“ vfbdj mWo“ v?lnrs ‚ m?ƒ „ r3lnk‰ v,lnrsbdt ~*r3lna?v e\mW~o1‰ kub?onbdƒ „ _?bfjurs_?» Q′￥o“ v?… ¸ rsi(vfzfm?ƒ Ibf}Tl“ vfrstTm w¸ ~*r3lna0v¼ l„ o“ v?_W½ ‚ m?w0g?′† v?oni?” ￥lhrsi?jTonb, ‚ mpƒ ?kTj bdt?mp¾ Lrsƒ ’ lnrstuz?i?mplnaub`wLƒ hv?t‰ w?b, ‚ mpon_?bfi?m?ƒ yi?bdƒ ’ l?bfeKlnaum?r3o?r¶ tYl„ onrstuƒ „ rs_ ƒ „ j m?m?wKˆ uonbf}ukTƒ ’ lntum?ƒ „ ƒ ?ˆ uvftuw?vf_?_pkTo“ vf_W… ?jTonbf}ucsm?i?ƒ ?” T&#
Update : 2025-02-19 Size : 285kb Publisher : 刘坤
[Linux-Unix] vgic-v3 DL : 0: LRs are stored in reverse order in memory. make sure we index them correctly.
Update : 2025-02-19 Size : 2kb Publisher : fuipunher
[Linux-Unix] vgic-v3-switch DL : 0: We store LRs in reverse order to let the CPU deal with streaming access. Use this macro to make it look sane.
Update : 2025-02-19 Size : 2kb Publisher : zkcongck
[matlab] LRS DL : 0: RLS 递归最小二乘滤波器算法！！！！！！！！！！！！！！！！！！！！！(Recursive least squares (RLS) is an adaptive filter algorithm that recursively finds the coefficients that minimize a weighted linear least squares cost function relating to the input signals. This approach is in contrast to other algorithms such as the least mean squares (LMS) that aim to reduce the mean square error. In the derivation of the RLS, the input signals are considered deterministic, while for the LMS and similar algorithm they are considered stochastic. Compared to most of its competitors, the RLS exhibits extremely fast convergence. However, this benefit comes at the cost of high computational complexity.)
Update : 2025-02-19 Size : 1kb Publisher : 清微