Routine mar1psd: To compute the power spectum by AR-model parameters. Input parameters: ip : AR model order (integer) ep : White noise variance of model input (real) ts : Sample interval in seconds (real) a : Complex array of AR parameters a(0) to a(ip) Output parameters: psdr : Real array of power spectral density values psdi : Real work array in chapter 12
标签： parameters AR-model Routine mar1psd
上传时间： 2015-06-09
上传用户：playboys0
This applet illustrates the prediction capabilities of the multi-layer perceptrons. It allows to define an input signal on which prediction will be performed. The user can choose the number of input units, hidden units and output units, as well as the delay between the input series and the predicted output series. Then it is possible to observe interesting prediction properties.
标签： capabilities illustrates multi-layer perceptrons
上传时间： 2015-06-17
上传用户：lnnn30
Wavelets have widely been used in many signal and image processing applications. In this paper, a new serial-parallel architecture for wavelet-based image compression is introduced. It is based on a 4-tap wavelet transform, which is realised using some FIFO memory modules implementing a pixel-level pipeline architecture to compress and decompress images. The real filter calculation over 4 · 4 window blocks is done using a tree of carry save adders to ensure the high speed processing required for many applications. The details of implementing both compressor and decompressor sub-systems are given. The primarily analysis reveals that the proposed architecture, implemented using current VLSI technologies, can process a video stream in real time.
标签： applications processing Wavelets widely
上传时间： 2014-01-22
上传用户：hongmo
光学设计软件zemax源码： This DLL models an nular aspheric surface as described in: "Annular surfaces in annular field systems" By Jose M. Sasian Opt. eng. 36 (12) P 3401-3401 December 1997 This surface is essentially an odd aspheric surface with an offset in the aspheric terms. The sag is given by: Z = (c*r*r) / (1+(1-((1+k)*c*c*r*r))^ 1/2 ) + a*(r-q)^2 + b*(r-q)^3 + c*(r-q)^4 + ... Note the terms a, b, c, ... have units of length to the -1, -2, -3, ... power.
标签： described aspheric surfaces Annular
上传时间： 2014-01-08
上传用户：yyyyyyyyyy
1、 了解系统调用pipe()的功能和实际原理 2、 编写一段程序，使用管道实现父子进程之间的通信 a) 使用系统调用fork()创建一个子进程 b) 子进程调用函数write()向父进程发送自己的进程ID和字符串” s sending a message to parent.\n”。 c) 父进程调用函数read()通过管道读出子进程发来的消息，将消息输出屏幕，然后终止
上传时间： 2013-12-16
上传用户：古谷仁美
1、 了解系统调用fork()、execl()、exit()、getpid()和waitpid()的功能和实现过程 2、 编写一段程序实现以下功能： a) 使用系统调用fork()创建两个子进程 b) 父进程重复显示字符串”parent:”，并使用函数getpid()显示自己的进程ID。 c) 两个子进程分别重复显示字符串”child:”，并使用函数getpid()显示自己的进程ID 3、 编写一段程序实现以下功能： a) 使用系统调用fork()创建一个子进程 b) 子进程显示自己的进程ID和字符串": The child is calling an exec.\n"，然后通过execl()调用系统命令ps显示当前运行的进程情况，从而更换自己的执行代码，最后调用exit()结束。 c) 父进程显示自己的进程ID和字符串” ": The parent is waiting for child to exit.\n "，然后调用waitpid()等待子进程结束，并在子进程结束后显示”The parent exit.\n
上传时间： 2013-12-18
上传用户：叶山豪
收SP下行消息 A. 启动MMSC侦听端口 在模拟器界面的右下角的"Liten Port"文本框中输入MMSC的侦听端口，这个值是为接收SP发出的下行消息提供服务的端口号，比如："8080"，按下"Start"按钮启动MMSC侦听服务。 B. 接收消息 接收的是从SP（API）发来的消息，处理后回一条响应消息。 2 模拟MMSC向SP发送上行消息 A. 选择模拟器左边界面的MessageType为“DeliverReq”； B. “Send To”文本框中输入SP的上行地址，例如http://10.164.50.29:8888； C. 在界面中选择输入其他需要的字段，然后点击“Send”按纽即可向SP上行地址发送上行消息。 3 模拟MMSC向SP发送递送报告消息 A. 选择模拟器左边界面的MessageType为“DeliverReportReq”； B. “Send To”文本框中输入SP的上行地址，例如http://10.164.50.29:8888； C. 在界面中选择输入其他需要的字段，然后点击“Send”按纽即可向SP上行地址发送递送报告消息。 4 模拟MMSC向SP发送阅读报告消息 A. 选择模拟器左边界面的MessageType为“ReadReportReq”； B. “Send To”文本框中输入SP的上行地址，例如http://10.164.50.29:8888； C. 在界面中选择输入其他需要的字段，然后点击“Send”按纽即可向SP上行地址发送阅读报告消息
上传时间： 2014-01-16
上传用户：气温达上千万的
MULTIDIMENSIONAL SCALING in matlab by Mark Steyvers 1999 ％needs optimization toolbox ％Modified by Bruce Land ％--Data via globals to anaylsis programs ％--3D plotting with color coded groups ％--Mapping of MDS space to spike train temporal profiles as described in ％Aronov, et.al. "Neural coding of spatial phase in V1 of the Macaque" in ％press J. Neurophysiology
标签： MULTIDIMENSIONAL optimization Modified Steyvers
上传时间： 2015-08-26
上传用户：kytqcool
Knowledge of the process noise covariance matrix is essential for the application of Kalman filtering. However, it is usually a difficult task to obtain an explicit expression of for large time varying systems. This paper looks at an adaptive Kalman filter method for dynamic harmonic state estimation and harmonic injection tracking.
标签： application covariance Knowledge essential
上传时间： 2014-01-19
上传用户：litianchu
This paper deals with the problem of speech enhancement when a corrupted speech signal with an additive colored noise is the only information available for processing. Kalman filtering is known as an effective speech enhancement technique, in which speech signal is usually modeled as autoregressive (AR) process and represented in the state-space domain.
标签： speech with enhancement corrupted
上传时间： 2015-09-07
上传用户：zhangyi99104144
