## Wednesday, March 28, 2012

### Windows 7无法休眠？

（休眠是Hibernate 睡眠是sleep 哈哈~）

P.S. 这个帖子的回复也值得一看~

## Monday, March 26, 2012

### jQuery中设置click事件的参数

(部分转自百度知道)

【问题】

<a href="#" onClick="showFile('view');">aaaaa</a>

<script>function showFile(fun){}</script>

<a href="#" id="fun">aaaaa</a>$("#fun").click(function () { }); 解答】 1. jQuery的click事件不能直接传递参数， 如果使用$('#fun').click(choose("val"));

});

$('#fun').click(function () { function()(调用的方法) }); 3. 可以使用标签内的attr来获得关于此标签的参数 <a id="fun" testvalue='abc' href="#" onClick="showFile('view');">aaaaa</a>$('#fun').click(function () {

alert($(this).attr('testvalue')); alert($(this).text());
alert($(this).attr('href')); //...... }); ### jQuery获取input标签的值 一般来说，用js获取input标签内的值会用 <input id="p_folder"></input> var p = document.getElementById("p_folder"); var pV = p.value; 但是jQuery中，如果写成 var p =$('p_folder');
var pV = p.value；

$("")是一个jQuery对象，而不是一个DOM element value是DOM element的属性，对应jQuery的val val():获得第一个匹配元素的当前值 val("val"):设置每一个匹配元素的值为val 所以上面的code应该写成 var p =$('p_folder');
var pV = p.val();

## Friday, March 2, 2012

### Summary of Compressed Video Sensing

[1] proposed to use cs on stream video by sample several frame together or independently.... But it didn't consider the interframe redundancy.

[2] was focus on increasing the resolution of digital video, thus little work was done for video coding/compression.

[3] [4] proposed compressed video sensing in  2008.
[3] used a hybrid way to compress video. The main contribution I think was only the scheme it proposed: transmit both conventionally encoding(low resolution) and cs encoding(high resolution) video stream, recon. on demand (if coarse-scale -> conventionally decoding, if fine-scale, cs decoding).
Compared to [3], I think [4] is much important for CVS. The way it employed is classifying the blocks of a frame to dense and sparse via a cs testing. Dense blocks use conventional encoding, while sparse blocks use cs. The cs testing for a block of frame is another contribution should be noticed.

In 2009, most work were focus on distributed CVS based on the notion of Distributed Video Coding(DVC). [5] use reconstructed key frame to find sparse basis for cs frame, and it proposed L1, SKIP, SINGLE modes for cs frames. The codec is quite similar with pixel-domain dvc. [6] also use reconstructed key frame to generate side information. But its side information is not the sparse basis, but a prediction. Furthermore, [6] use both frame-based and block-based encoding for cs frames. It is quite novel. But I think although it improves the performance, but a little redundant. Different with [5][6], [7] use cs for both key frame and non-key frame. And it proposed the modified GPSR for DCVS. Furthermore, there are relatively complete review of techniques like cs, dvc, dcs, etc., which I think is quite useful for beginners in this area.

[8] proposed a very interesting multiscale framework. It employs LIMAT[11] framework to exploit motion information and remove temporal redundancies. And it use iterative multiscale framework: reconstructing successively finer resolution approximation to each frame using motion vectors estimated at coarser scales, and alternatively using these approximation to estimate the motion. The multiscale framework essentially exploit the feature of wavelet transformation (coarse scale and fine scale).

[10] is published in 2011. It designs the cross-layer system for video transmission using compressed sensing.
The cross-layer system jointly controls video encoding rate, transmission rate, channel coding rate. It is useful for researchers who focus on network design of a compressive sensing application.

[9] is not about CVS, but I think it's very important to know current video compression techniques. It introduced the video compression techniques like H.26x, MPEG, etc. It's a very good introduction and review work.

Another thing should be mentioned is that, Distributed Compressed Video Coding in [5] [6] both used the notion: the sparsest representation of a frame is a combination of neighbor blocks of a block.

[1] Compressive imaging for video representation and coding
[2] Compressive coded aperture video reconstruction
[3] Compressed video sensing
[4] Compressive Video Sampling
[5] Distributed video coding using compressive sampling
[6] Distributed compressed video sensing
[7] Distributed compressive video sensing
[8] A multiscale framework for compressive sensing of video
[9] Video Compression Techniques: An Overview
[10] Compressed-Sensing-Enabled Video Streaming for Wireless Multimedia Sensor Networks
[11] Lifting-based invertible motion adaptive transform framework for highly scalable video compression

### Review of the size of Measurement Matrix in Compressed Sensing

In the following description, M is the number of measurements needed, N is the length of signal, K is sparsity, Φ is measurement matrix, Ψ is sparse basis. C is a constant.
$Y = AX = \Phi\Psi X$

The requirement for $M$
[1] Sparsity and Incoherence in compressive sampling
$M \geq C \cdot \mu^2(\Phi, \Psi) \cdot K {\text log}N$

[2] An introduction to Compressive Sampling
Form A obeying RIP i)-iv)
$M = O(K {\text log}(N/K))$
$M \geq C \cdot K {\text log}(N/K)$
i)-iii) see
[3] A simple proof of the restricted isometry property for random matrices
iv) see
[4] Uniform uncertainty principles for Bernoulli and sub-gaussian ensembles

Form A by first finding paris of incoherent orthobases $\Phi, \Psi$, and then exracting $M$ coordinates uniformly at random using R: $A = R\Phi\Psi$.
$M \geq C \cdot ({\text log}N)^4$
$M \geq C \cdot ({\text log}N)^5$ for a lower probability of failure
see [6] and [7] On sparse reconstruction from Fourier and Gaussian measurements

[6] Near-optimal signal recovery from random projections and universal encoding strategies
[8] Compressed Sensing, D.L.Donoho
[9] Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
[10] Neighborliness of randomly projected simplices in high dimensions
[11] High-dimensional centrally symmetreic polytopes with neighborliness proportional to dimension
$M = O(K {\text log}(N))$
$M \geq C \cdot K \cdot {\text log}N$

[12] Compressive Sensing, R.G.Baranuik
$M \geq C \cdot K {\text log}(N/K)$
it cited the result from [8] and [9]. Are they the same?

To summary, there are 4 expressions:
1)
$M = O(K {\text log}(N))$
$M \geq C \cdot K \cdot {\text log}N$
2)
$M \geq C \cdot K {\text log}(N/K)$
3)
$M \geq C \cdot ({\text log}N)^4$
$M \geq C \cdot ({\text log}N)^5$
4)
$M \geq C \cdot \mu^2(\Phi, \Psi) \cdot K {\text log}N$

1) and 2) are quite similar with each other; 1) is noisy situation and 2) is noiseless.
4) is quite similar with 1), except the parameter $\mu^2(\Phi, \Psi)$, which is a measure for the incoherence between the two matrix.

## Thursday, March 1, 2012

### Some Ideas

* 这个还可以开发app平台 做一个app推荐菜谱 or 根据家里的菜规划要做的菜（对于一次买N天菜的人） 提醒菜过期...