This is about the LCL filter design,LCL 濾波器設計
上傳時間： 2015-06-23
上傳用戶：jokermo
This is ieee transaction Paper about LCL filter design
標簽： LCL design
上傳時間： 2015-06-23
上傳用戶：jokermo
matlab有限元網格劃分程序 DistMesh is a simple MATLAB code for generation of unstructured triangular and tetrahedral meshes. It was developed by Per-Olof Persson (now at UC Berkeley) and Gilbert Strang in the Department of Mathematics at MIT. A detailed description of the program is provided in our SIAM Review Paper, see documentation below. One reason that the code is short and simple is that the geometries are specified by Signed Distance Functions. These give the shortest distance from any point in space to the boundary of the domain. The sign is negative inside the region and positive outside. A simple example is the unit circle in 2-D, which has the distance function d=r-1, where r is the distance from the origin. For more complicated geometries the distance function can be computed by interpolation between values on a grid, a common representation for level set methods. For the actual mesh generation, DistMesh uses the Delaunay triangulation routine in MATLAB and tries to optimize the node locations by a force-based smoothing procedure. The topology is regularly updated by Delaunay. The boundary points are only allowed to move tangentially to the boundary by projections using the distance function. This iterative procedure typically results in very well-shaped meshes. Our aim with this code is simplicity, so that everyone can understand the code and modify it according to their needs. The code is not entirely robust (that is, it might not terminate and return a well-shaped mesh), and it is relatively slow. However, our current research shows that these issues can be resolved in an optimized C++ code, and we believe our simple MATLAB code is important for demonstration of the underlying principles. To use the code, simply download it from below and run it from MATLAB. For a quick demonstration, type "meshdemo2d" or "meshdemond". For more details see the documentation.
標簽： matlab有限元網格劃分程序
上傳時間： 2015-08-12
上傳用戶：凜風拂衣袖
We consider the problem of target localization by a network of passive sensors. When an unknown target emits an acoustic or a radio signal, its position can be localized with multiple sensors using the time difference of arrival (TDOA) information. In this Paper, we consider the maximum likelihood formulation of this target localization problem and provide efficient convex relaxations for this nonconvex optimization problem.We also propose a formulation for robust target localization in the presence of sensor location errors. Two Cramer-Rao bounds are derived corresponding to situations with and without sensor node location errors. Simulation results confirm the efficiency and superior performance of the convex relaxation approach as compared to the existing least squares based approach when large sensor node location errors are present.
標簽： 傳感器網絡
上傳時間： 2016-11-27
上傳用戶：xxmluo
微軟的FAT32官方資料中文翻譯版。搞嵌入式FAT32很有用的。
標簽： FAT32
上傳時間： 2016-12-26
上傳用戶：CrownZeng
最小角回歸英文原始Paper，可以參考的地方很多
上傳時間： 2017-02-17
上傳用戶：csckd
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
DEEP learning Paper DEEP learning Paper DEEP learning Paper DEEP learning Paper DEEP learning Paper DEEP learning Paper DEEP learning Paper DEEP learning Paper
上傳時間： 2018-06-13
上傳用戶：1203955829@qq.com
1、 選擇合適的SCI期刊-Choose a journal。結合專業知識、2008或2007年度影響因子表和他人經驗來綜合選擇要投遞的期刊，并進入該期刊查詢系統查詢近年來的文章走向。 2、 下載Introduction for submission。只要到每個雜志的首頁，打開submit Paper一欄，點擊Introduction查看或下載即可。 3、 稿件及其相關材料準備-Preparation：Manuscript.doc、Tables.doc、Figures.tiff（jpg等）、Cover letter，有時還有Title page、Copyright agreement、Conflicts of interest等。
標簽： 論文
上傳時間： 2018-08-17
上傳用戶：pengke871218
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
