PhD research, you have already made a decision that will have a major impact on the success of your project, and perhaps even on your future career. You have chosen to work in a particular research group, under the guidance of a particular thesis advisor or supervisor.
標簽： have research decision already
上傳時間： 2017-09-14
上傳用戶：cursor
Before you even get started on your PhD research, you have already made a decision that will have a major impact on the success of your project, and perhaps even on your future career. You have chosen to work in a particular research group, under the guidance of a particular thesis advisor or supervisor.
標簽： have you decision research
上傳時間： 2013-11-28
上傳用戶：asdfasdfd
Features a unique program to estimate the power spectral density. The spectrum containing all significant details is calculated from a time series model. Model type as well as model order are determined automatically from the data, using statistical criteria. Robust estimation algorithms and order selection criteria are used to obtain reliable results. Unlike in FFT analysis, where the experimenter has to set the amount of smoothing of the raw FFT, the right level of detail is assessed using the data only.
標簽： containing Features estimate spectral
上傳時間： 2014-02-09
上傳用戶：daguda
Complete solution for Hardware Programming. PonyProg software and Schematics. Contains PCBs and all the hardware diagrams needed by the Hardware Programmer. PCBs are tested and Software which is a Freeware, Works well under Windows XP and Windows Vista.
標簽： Programming Schematics and Complete
上傳時間： 2013-12-31
上傳用戶：123啊
V1.16 Win32 July 2012 - Ported to Win32 C++ - Allow multiple instances of libnids to coexist in the same process - Incorporate unofficial patch to track established TCP connections - Migration of calls to secure versions (i.e. strcpy to strcpy_s) - Compiles under Visual Studio 2010 with no warnings at W4 - Linux support well and truly broken, Linux specific code removed
標簽： libnids-Win32
上傳時間： 2016-07-30
上傳用戶：mxgg126
The 4.0 kbit/s speech codec described in this paper is based on a Frequency Domain Interpolative (FDI) coding technique, which belongs to the class of prototype waveform Interpolation (PWI) coding techniques. The codec also has an integrated voice activity detector (VAD) and a noise reduction capability. The input signal is subjected to LPC analysis and the prediction residual is separated into a slowly evolving waveform (SEW) and a rapidly evolving waveform (REW) components. The SEW magnitude component is quantized using a hierarchical predictive vector quantization approach. The REW magnitude is quantized using a gain and a sub-band based shape. SEW and REW phases are derived at the decoder using a phase model, based on a transmitted measure of voice periodicity. The spectral (LSP) parameters are quantized using a combination of scalar and vector quantizers. The 4.0 kbits/s coder has an algorithmic delay of 60 ms and an estimated floating point complexity of 21.5 MIPS. The performance of this coder has been evaluated using in-house MOS tests under various conditions such as background noise. channel errors, self-tandem. and DTX mode of operation, and has been shown to be statistically equivalent to ITU-T (3.729 8 kbps codec across all conditions tested.
標簽： frequency-domain interpolation performance Design kbit_s speech coder based and of
上傳時間： 2018-04-08
上傳用戶：kilohorse
function [alpha,N,U]=youxianchafen2(r1,r2,up,under,num,deta) %[alpha,N,U]=youxianchafen2(a,r1,r2,up,under,num,deta) %該函數用有限差分法求解有兩種介質的正方形區域的二維拉普拉斯方程的數值解 %函數返回迭代因子、迭代次數以及迭代完成后所求區域內網格節點處的值 %a為正方形求解區域的邊長 %r1,r2分別表示兩種介質的電導率 %up,under分別為上下邊界值 %num表示將區域每邊的網格剖分個數 %deta為迭代過程中所允許的相對誤差限 n=num+1; %每邊節點數 U(n,n)=0; %節點處數值矩陣 N=0; %迭代次數初值 alpha=2/(1+sin(pi/num));%超松弛迭代因子 k=r1/r2; %兩介質電導率之比 U(1,1:n)=up; %求解區域上邊界第一類邊界條件 U(n,1:n)=under; %求解區域下邊界第一類邊界條件 U(2:num,1)=0;U(2:num,n)=0; for i=2:num U(i,2:num)=up-(up-under)/num*(i-1);%采用線性賦值對上下邊界之間的節點賦迭代初值 end G=1; while G>0 %迭代條件：不滿足相對誤差限要求的節點數目G不為零 Un=U; %完成第n次迭代后所有節點處的值 G=0; %每完成一次迭代將不滿足相對誤差限要求的節點數目歸零 for j=1:n for i=2:num U1=U(i,j); %第n次迭代時網格節點處的值 if j==1 %第n+1次迭代左邊界第二類邊界條件 U(i,j)=1/4*(2*U(i,j+1)+U(i-1,j)+U(i+1,j)); end if (j>1)&&(j U2=1/4*(U(i,j+1)+ U(i-1,j)+ U(i,j-1)+ U(i+1,j)); U(i,j)=U1+alpha*(U2-U1); %引入超松弛迭代因子后的網格節點處的值 end if i==n+1-j %第n+1次迭代兩介質分界面（與網格對角線重合）第二類邊界條件 U(i,j)=1/4*(2/(1+k)*(U(i,j+1)+U(i+1,j))+2*k/(1+k)*(U(i-1,j)+U(i,j-1))); end if j==n %第n+1次迭代右邊界第二類邊界條件 U(i,n)=1/4*(2*U(i,j-1)+U(i-1,j)+U(i+1,j)); end end end N=N+1 %顯示迭代次數 Un1=U; %完成第n+1次迭代后所有節點處的值 err=abs((Un1-Un)./Un1);%第n+1次迭代與第n次迭代所有節點值的相對誤差 err(1,1:n)=0; %上邊界節點相對誤差置零 err(n,1:n)=0; %下邊界節點相對誤差置零 G=sum(sum(err>deta))%顯示每次迭代后不滿足相對誤差限要求的節點數目G end
標簽： 有限差分
上傳時間： 2018-07-13
上傳用戶：Kemin
Introduction jSMPP is a java implementation (SMPP API) of the SMPP protocol (currently supports SMPP v3.4). It provides interfaces to communicate with a Message Center or an ESME (External Short Message Entity) and is able to handle traffic of 3000-5000 messages per second. jSMPP is not a high-level library. People looking for a quick way to get started with SMPP may be better of using an abstraction layer such as the Apache Camel SMPP component: http://camel.apache.org/smpp.html Travis-CI status: History The project started on Google Code: http://code.google.com/p/jsmpp/ It was maintained by uudashr on Github until 2013. It is now a community project maintained at http://jsmpp.org Release procedure mvn deploy -DperformRelease=true -Durl=https://oss.sonatype.org/service/local/staging/deploy/maven2/ -DrepositoryId=sonatype-nexus-staging -Dgpg.passphrase=<yourpassphrase> log in here: https://oss.sonatype.org click the 'Staging Repositories' link select the repository and click close select the repository and click release License Copyright (C) 2007-2013, Nuruddin Ashr uudashr@gmail.com Copyright (C) 2012-2013, Denis Kostousov denis.kostousov@gmail.com Copyright (C) 2014, Daniel Pocock http://danielpocock.com Copyright (C) 2016, Pim Moerenhout pim.moerenhout@gmail.com This project is licensed under the Apache Software License 2.0.
上傳時間： 2019-01-25
上傳用戶：dragon_longer
Reconstruction- and example-based super-resolution (SR) methods are promising for restoring a high-resolution (HR) image from low-resolution (LR) image(s). Under large magnification, reconstruction-based methods usually fail to hallucinate visual details while example-based methods sometimes introduce unexpected details. Given a generic LR image, to reconstruct a photo-realistic SR image and to suppress artifacts in the reconstructed SR image, we introduce a multi-scale dictionary to a novel SR method that simultaneously integrates local and non-local priors. The local prior suppresses artifacts by using steering kernel regression to predict the target pixel from a small local area. The non-local prior enriches visual details by taking a weighted average of a large neighborhood as an estimate of the target pixel. Essentially, these two priors are complementary to each other. Experimental results demonstrate that the proposed method can produce high quality SR recovery both quantitatively and perceptually.
標簽： Super-resolution Multi-scale Dictionary Single Image for
上傳時間： 2019-03-28
上傳用戶：fullout
In this paper we present a classifier called bi-density twin support vector machines (BDTWSVMs) for data classification. In the training stage, BDTWSVMs first compute the relative density degrees for all training points using the intra-class graph whose weights are determined by a local scaling heuristic strategy, then optimize a pair of nonparallel hyperplanes through two smaller sized support vector machine (SVM)-typed problems. In the prediction stage, BDTWSVMs assign to the class label depending on the kernel density degree-based distances from each test point to the two hyperplanes. BDTWSVMs not only inherit good properties from twin support vector machines (TWSVMs) but also give good description for data points. The experimental results on toy as well as publicly available datasets indicate that BDTWSVMs compare favorably with classical SVMs and TWSVMs in terms of generalization
標簽： recognition Bi-density machines support pattern vector twin for
上傳時間： 2019-06-09
上傳用戶：lyaiqing
