<?xml version="1.0" encoding="utf-8" ?><rss version="2.0"><channel><title><![CDATA[sinat_35637310的博客]]></title><description><![CDATA[]]></description><link>https://blog.youkuaiyun.com/sinat_35637310</link><language>zh-cn</language><generator>https://blog.youkuaiyun.com/</generator><copyright><![CDATA[Copyright &copy; sinat_35637310]]></copyright><item><title><![CDATA[mobaXterm使用pycharm]]></title><link>https://blog.youkuaiyun.com/sinat_35637310/article/details/132754725</link><guid>https://blog.youkuaiyun.com/sinat_35637310/article/details/132754725</guid><author>sinat_35637310</author><pubDate>Fri, 08 Sep 2023 10:22:25 +0800</pubDate><description><![CDATA[首先去pycharm的官网。]]></description><category></category></item><item><title><![CDATA[T检验与Z检验的区别]]></title><link>https://blog.youkuaiyun.com/sinat_35637310/article/details/120689777</link><guid>https://blog.youkuaiyun.com/sinat_35637310/article/details/120689777</guid><author>sinat_35637310</author><pubDate>Sun, 10 Oct 2021 18:22:55 +0800</pubDate><description><![CDATA[概念区别：T检验，亦称student t检验（Student's t test），主要用于样本含量较小（例如n&lt;30），总体标准差σ未知的正态分布资料。Z检验是一般用于大样本(即样本容量大于30)平均值差异性检验的方法。它是用标准正态分布的理论来推断差异发生的概率，从而比较两个平均数平均数的差异是否显著。

区别一：z检验适用于变量符合z分布的情况，而t检验适用于变量符合t分布的情况；

区别二：t分布是z分布的小样本分布，即当总体符合z分布时，从总体中抽取的小样本符合t分布，而对于符合t分布的变量，]]></description><category></category></item><item><title><![CDATA[scipy.io.matlab.miobase.MatWriteError: Matrix too large to save with Matlab 5 format]]></title><link>https://blog.youkuaiyun.com/sinat_35637310/article/details/120417675</link><guid>https://blog.youkuaiyun.com/sinat_35637310/article/details/120417675</guid><author>sinat_35637310</author><pubDate>Wed, 22 Sep 2021 16:41:07 +0800</pubDate><description><![CDATA[python 读写Matlab mat v7.3文件

Introduction — hdf5storage 0.1.14 documentation

问题：
一开始想使用scipy.io保存.mat文件(sio.savemat())，但是疯狂报错MatWriteError: Matrix too large to save with Matlab 5 format

解决方案：
使用hdf5storage.savemat()即可

具体方法：


pip install hdf5storage


# ]]></description><category></category></item><item><title><![CDATA[2021-06-17 topological FDR in SPM]]></title><link>https://blog.youkuaiyun.com/sinat_35637310/article/details/118003500</link><guid>https://blog.youkuaiyun.com/sinat_35637310/article/details/118003500</guid><author>sinat_35637310</author><pubDate>Thu, 17 Jun 2021 22:38:24 +0800</pubDate><description><![CDATA[SPM8允许两种FDR校验。一个是voxel-wise FDR，另一个是topological FDR。

如果要做voxel-wise FDR 校验，就把spm_defaults里的68行的defaults.stats.topoFDR 改为0。

此时SPM8里的FDR按钮会出现。在SPM里点results，选FDR校验即可。

如果要做topological FDR 校验，就把defaults.stats.topoFDR设为1（默认是1）。

首先在SPM8里点results——None（无校验）——T]]></description><category></category></item><item><title><![CDATA[2021-04-25 宋体字号与英文磅值之间的转换]]></title><link>https://blog.youkuaiyun.com/sinat_35637310/article/details/116139796</link><guid>https://blog.youkuaiyun.com/sinat_35637310/article/details/116139796</guid><author>sinat_35637310</author><pubDate>Sun, 25 Apr 2021 22:30:49 +0800</pubDate><description><![CDATA[字号‘八号’对应磅值5
字号‘七号’对应磅值5.5
字号‘小六’对应磅值6.5
字号‘六号’对应磅值7.5
字号‘小五’对应磅值9
字号‘五号’对应磅值10.5
字号‘小四’对应磅值12
字号‘四号’对应磅值14
字号‘小三’对应磅值15
字号‘三号’对应磅值16
字号‘小二’对应磅值18
字号‘二号’对应磅值22
字号‘小一’对应磅值24
字号‘一号’对应磅值26
字号‘小初’对应磅值36
字号‘初号’对应磅值42
...]]></description><category></category></item><item><title><![CDATA[Windows远程连接linux服务器出现闪退]]></title><link>https://blog.youkuaiyun.com/sinat_35637310/article/details/115531050</link><guid>https://blog.youkuaiyun.com/sinat_35637310/article/details/115531050</guid><author>sinat_35637310</author><pubDate>Thu, 08 Apr 2021 20:49:32 +0800</pubDate><description><![CDATA[在树莓派 ubuntu18.04上用其他教程安装Xrdp出现了闪退的情况

原始桌面ubuntu-desktop


echo gnome-session &gt; ~/.xsession

xfce4桌面xubuntu-desktop


echo xfce4-session &gt; ~/.xsession

亲测有效！！！


安装教程：https://blog.youkuaiyun.com/cxn304/article/details/99733711

实用




...]]></description><category></category></item><item><title><![CDATA[Windows远程连接linux服务器出现身份验证错误]]></title><link>https://blog.youkuaiyun.com/sinat_35637310/article/details/115529438</link><guid>https://blog.youkuaiyun.com/sinat_35637310/article/details/115529438</guid><author>sinat_35637310</author><pubDate>Thu, 08 Apr 2021 19:46:43 +0800</pubDate><description><![CDATA[在 windows 系统里通过远程桌面（mstsc）远程访问 Ubuntu系统的xrdp出现：
发生身份验证错误，给函数提供的标志无效





需要修改 /etc/xrdp/xrdp.ini


sudo vim /etc/xrdp/xrdp.ini



; set SSL protocols
; can be comma separated list of 'SSLv3', 'TLSv1', 'TLSv1.1', 'TLSv1.2', 'TLSv1.3'
ssl_protocols=TLSv1

将.]]></description><category></category></item><item><title><![CDATA[使用numpy时警告：RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility]]></title><link>https://blog.youkuaiyun.com/sinat_35637310/article/details/113573170</link><guid>https://blog.youkuaiyun.com/sinat_35637310/article/details/113573170</guid><author>sinat_35637310</author><pubDate>Tue, 02 Feb 2021 20:11:34 +0800</pubDate><description><![CDATA[在安装numpy之后运行python程序时可能会遇到以下警告：


/home/X/.local/lib/python2.7/site-packages/scipy/signal/__init__.py:314: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 80
  from ._max_len_seq import max_len_seq
/home/X/.l]]></description><category></category></item><item><title><![CDATA[cifar10 数据集介绍]]></title><link>https://blog.youkuaiyun.com/sinat_35637310/article/details/97611856</link><guid>https://blog.youkuaiyun.com/sinat_35637310/article/details/97611856</guid><author>sinat_35637310</author><pubDate>Sun, 28 Jul 2019 10:38:43 +0800</pubDate><description><![CDATA[基本信息

CIFAR-10 是一个包含60000张图片的数据集。其中每张照片为32*32的彩色照片，每个像素点包括RGB三个数值，数值范围 0 ~ 255。所有照片分属10个不同的类别，分别是 'airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck'。其中五万张图片被划分为训练...]]></description><category></category></item><item><title><![CDATA[matlab函数_连通区域]]></title><link>https://blog.youkuaiyun.com/sinat_35637310/article/details/83054733</link><guid>https://blog.youkuaiyun.com/sinat_35637310/article/details/83054733</guid><author>sinat_35637310</author><pubDate>Mon, 15 Oct 2018 10:04:09 +0800</pubDate><description><![CDATA[1、 matlab函数bwareaopen──删除小面积对象
格式：BW2 = bwareaopen(BW,P,conn)
作用：删除二值图像BW中面积小于P的对象，默认情况下使用8邻域。
算法：
(1)Determine the connected components.
  L = bwlabeln(BW, conn);
(2)Compute the area of each componen...]]></description><category></category></item><item><title><![CDATA[MATLAB图像处理相关命令]]></title><link>https://blog.youkuaiyun.com/sinat_35637310/article/details/83054455</link><guid>https://blog.youkuaiyun.com/sinat_35637310/article/details/83054455</guid><author>sinat_35637310</author><pubDate>Mon, 15 Oct 2018 10:00:06 +0800</pubDate><description><![CDATA[一、通用函数：

colorbar  显示彩色条

语法：colorbar \ colorbar('vert') \ colorbar('horiz') \ colorbar(h) \ h=colorbar(...) \ colorbar(...,'peer',axes_handle)

getimage 从坐标轴取得图像数据

语法：A=getimage(h) \ [x,y,A]=getimag...]]></description><category></category></item><item><title><![CDATA[MATLAB 绘图函数(二)]]></title><link>https://blog.youkuaiyun.com/sinat_35637310/article/details/82967900</link><guid>https://blog.youkuaiyun.com/sinat_35637310/article/details/82967900</guid><author>sinat_35637310</author><pubDate>Mon, 08 Oct 2018 15:17:04 +0800</pubDate><description><![CDATA[一、MATLAB通用图形函数命令

有关命令行环境的一些操作: 

(1) clc 擦去一页命令窗口,光标回屏幕左上角 (2) clear 从工作空间清除所有变量 (3) clf 清除图形窗口内容

命令1  figure

功能  创建一个新的图形对象。图形对象为在屏幕上单独的窗口，在窗口中可以输出图形。

用法  figure 用缺省的属性值创建一个新的图形对象。

命令2  subplot
...]]></description><category></category></item><item><title><![CDATA[MATLAB 绘图函数(一)]]></title><link>https://blog.youkuaiyun.com/sinat_35637310/article/details/82967231</link><guid>https://blog.youkuaiyun.com/sinat_35637310/article/details/82967231</guid><author>sinat_35637310</author><pubDate>Mon, 08 Oct 2018 14:44:16 +0800</pubDate><description><![CDATA[基本函数

1.绘制二维曲线的最基本函数 plot

2.双纵坐标函数 plotyy

3.坐标控制        函数的调用格式为：    axis([xmin xmax ymin ymax zmin zmax])
    axis函数功能丰富，常用的用法还有：      axis equal 纵、横坐标轴采用等长刻度      axis square 产生正方形坐标系(缺省为矩形)
     ...]]></description><category></category></item><item><title><![CDATA[Working memory network （工作记忆网络）]]></title><link>https://blog.youkuaiyun.com/sinat_35637310/article/details/82350305</link><guid>https://blog.youkuaiyun.com/sinat_35637310/article/details/82350305</guid><author>sinat_35637310</author><pubDate>Mon, 10 Sep 2018 19:54:52 +0800</pubDate><description><![CDATA[working memory

Schlosser R G, Wagner G, Sauer H, et al. Assessing the working memory network: studies with functional magnetic resonance imaging and structural equation modeling.[J]. Neuroscience, 20...]]></description><category></category></item><item><title><![CDATA[Attention network （注意网络）]]></title><link>https://blog.youkuaiyun.com/sinat_35637310/article/details/82351006</link><guid>https://blog.youkuaiyun.com/sinat_35637310/article/details/82351006</guid><author>sinat_35637310</author><pubDate>Tue, 04 Sep 2018 10:33:43 +0800</pubDate><description><![CDATA[ 

Dorsal Attention Network 

pDLPFC / frontal eye fields
	Posterior parietal cortex: Superior parietal lobule (SPL) / Intra-parietal sulcus (IPS)
	rITC (above FPN region)
Functions: Selective attenti...]]></description><category></category></item><item><title><![CDATA[Executive functions （执行功能）]]></title><link>https://blog.youkuaiyun.com/sinat_35637310/article/details/82348710</link><guid>https://blog.youkuaiyun.com/sinat_35637310/article/details/82348710</guid><author>sinat_35637310</author><pubDate>Mon, 03 Sep 2018 16:11:41 +0800</pubDate><description><![CDATA[Executive functions

1. 组成

Neuroimaging and lesion studies have identified the functions which are most often associated with the particular regions of the prefrontal cortex.



The dorsolateral pref...]]></description><category></category></item><item><title><![CDATA[Default mode network （默认网络）]]></title><link>https://blog.youkuaiyun.com/sinat_35637310/article/details/82346533</link><guid>https://blog.youkuaiyun.com/sinat_35637310/article/details/82346533</guid><author>sinat_35637310</author><pubDate>Mon, 03 Sep 2018 14:50:22 +0800</pubDate><description><![CDATA[Default Mode Network

1. 组成

Functional hubs: Information regarding the self

Posterior cingulate cortex (PCC) &amp;amp; precuneus: Combines bottom-up (not controlled) attention with information from memo...]]></description><category></category></item><item><title><![CDATA[Salience network （显著网络）]]></title><link>https://blog.youkuaiyun.com/sinat_35637310/article/details/82318721</link><guid>https://blog.youkuaiyun.com/sinat_35637310/article/details/82318721</guid><author>sinat_35637310</author><pubDate>Sun, 02 Sep 2018 21:02:07 +0800</pubDate><description><![CDATA[Salience Network

1.组成

The salience network (SN) is a large scale brain network of the human brain that is primarily composed of the anterior insula (AI) and dorsal anterior cingulate cortex (dACC).
...]]></description><category></category></item><item><title><![CDATA[用于深度神经网络的语音信号预处理]]></title><link>https://blog.youkuaiyun.com/sinat_35637310/article/details/82221514</link><guid>https://blog.youkuaiyun.com/sinat_35637310/article/details/82221514</guid><author>sinat_35637310</author><pubDate>Thu, 30 Aug 2018 15:54:03 +0800</pubDate><description><![CDATA[1. 预备知识（基础概念）

    详见 https://blog.youkuaiyun.com/shanx_s/article/details/52947007

    详见 https://blog.youkuaiyun.com/Barry_J/article/details/80996564

2. 读取音频文件并进行音频特征提取

    2.1 用audioread(”);函数读取电脑中的音频文件，参数是音...]]></description><category></category></item><item><title><![CDATA[DeepNet深度学习框架的使用]]></title><link>https://blog.youkuaiyun.com/sinat_35637310/article/details/78864229</link><guid>https://blog.youkuaiyun.com/sinat_35637310/article/details/78864229</guid><author>sinat_35637310</author><pubDate>Thu, 21 Dec 2017 15:25:56 +0800</pubDate><description><![CDATA[http://blog.youkuaiyun.com/wspba/article/details/53365581]]></description><category></category></item></channel></rss>