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
標簽： parameters AR-model Routine mar1psd
上傳時間： 2015-06-09
上傳用戶：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.
標簽： capabilities illustrates multi-layer perceptrons
上傳時間： 2015-06-17
上傳用戶：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.
標簽： applications processing Wavelets widely
上傳時間： 2014-01-22
上傳用戶：hongmo
光學設計軟件zemax源碼： 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.
標簽： described aspheric surfaces Annular
上傳時間： 2014-01-08
上傳用戶：yyyyyyyyyy
1、 了解系統調用pipe()的功能和實際原理 2、 編寫一段程序，使用管道實現父子進程之間的通信 a) 使用系統調用fork()創建一個子進程 b) 子進程調用函數write()向父進程發送自己的進程ID和字符串” s sending a message to parent.\n”。 c) 父進程調用函數read()通過管道讀出子進程發來的消息，將消息輸出屏幕，然后終止
上傳時間： 2013-12-16
上傳用戶：古谷仁美
1、 了解系統調用fork()、execl()、exit()、getpid()和waitpid()的功能和實現過程 2、 編寫一段程序實現以下功能： a) 使用系統調用fork()創建兩個子進程 b) 父進程重復顯示字符串”parent:”，并使用函數getpid()顯示自己的進程ID。 c) 兩個子進程分別重復顯示字符串”child:”，并使用函數getpid()顯示自己的進程ID 3、 編寫一段程序實現以下功能： a) 使用系統調用fork()創建一個子進程 b) 子進程顯示自己的進程ID和字符串": The child is calling an exec.\n"，然后通過execl()調用系統命令ps顯示當前運行的進程情況，從而更換自己的執行代碼，最后調用exit()結束。 c) 父進程顯示自己的進程ID和字符串” ": The parent is waiting for child to exit.\n "，然后調用waitpid()等待子進程結束，并在子進程結束后顯示”The parent exit.\n
上傳時間： 2013-12-18
上傳用戶：葉山豪
收SP下行消息 A. 啟動MMSC偵聽端口 在模擬器界面的右下角的"Liten Port"文本框中輸入MMSC的偵聽端口，這個值是為接收SP發出的下行消息提供服務的端口號，比如："8080"，按下"Start"按鈕啟動MMSC偵聽服務。 B. 接收消息 接收的是從SP（API）發來的消息，處理后回一條響應消息。 2 模擬MMSC向SP發送上行消息 A. 選擇模擬器左邊界面的MessageType為“DeliverReq”； B. “Send To”文本框中輸入SP的上行地址，例如http://10.164.50.29:8888； C. 在界面中選擇輸入其他需要的字段，然后點擊“Send”按紐即可向SP上行地址發送上行消息。 3 模擬MMSC向SP發送遞送報告消息 A. 選擇模擬器左邊界面的MessageType為“DeliverReportReq”； B. “Send To”文本框中輸入SP的上行地址，例如http://10.164.50.29:8888； C. 在界面中選擇輸入其他需要的字段，然后點擊“Send”按紐即可向SP上行地址發送遞送報告消息。 4 模擬MMSC向SP發送閱讀報告消息 A. 選擇模擬器左邊界面的MessageType為“ReadReportReq”； B. “Send To”文本框中輸入SP的上行地址，例如http://10.164.50.29:8888； C. 在界面中選擇輸入其他需要的字段，然后點擊“Send”按紐即可向SP上行地址發送閱讀報告消息
上傳時間： 2014-01-16
上傳用戶：氣溫達上千萬的
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
標簽： MULTIDIMENSIONAL optimization Modified Steyvers
上傳時間： 2015-08-26
上傳用戶：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.
標簽： application covariance Knowledge essential
上傳時間： 2014-01-19
上傳用戶：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.
標簽： speech with enhancement corrupted
上傳時間： 2015-09-07
上傳用戶：zhangyi99104144
