Tuesday, April 14, 2009

[转]一定要知道的71个做饭技巧

1、煮水饺时,在水里放一颗大葱或在水开后加点盐,再放饺子,饺子味道鲜美不粘连;在和面时,每500克面粉加拌一个鸡蛋,饺子皮挺刮不粘连
2、 炖肉时,在锅里加上几块桔皮,可除异味和油腻并增加汤的鲜味
3、煮骨头汤时加一小匙醋,可使骨头中的磷、钙溶解于汤中,并可保存汤中的维生素。
4、炖鸡:洗净切块,倒入热油锅内翻炒,待水分炒干时,倒入适量香醋,再迅速翻炒,至鸡块发出劈劈啪啪的爆响声时,立即加热水(没过鸡块),再用旺火烧十分钟,即可放入调料,移小火上再炖20分钟,淋上香油即可出锅;应在汤炖好后,温度降至80~90摄氏度时或食用前加盐。因为鸡肉中含水分较高,炖鸡先加盐,鸡肉在盐水中浸泡,组织细胞内水分向外渗透,蛋白质产生凝固作用,使鸡肉明显收缩变紧,影响营养向汤内溶解,且煮熟后的鸡肉趋向硬、老,口感粗糙。
5、煮肉汤或排骨汤时,放入几块新鲜桔皮,不仅味道鲜美,还可减少油腻感。  
6、烧豆腐时,加少许豆腐乳或汁,味道芳香
7、将绿豆在铁锅中炒10分钟再煮能很快煮烂,但注意不要炒焦
8、煮蛋时水里加点醋可防蛋壳裂开,事先加点盐也可  
9、煮海带时加几滴醋易烂;放几棵波菜也行  
10、煮火腿之前,将火腿皮上涂些白糖,容易煮烂,味道更鲜美
11、羊肉去膻味:将萝卜块和羊肉一起下锅,半小时后取出萝卜块;放几块桔子皮更佳;每公斤羊肉放绿豆5克,煮沸10分钟后,将水和绿豆一起倒出;放半包山楂片;将带壳的核桃两三个洗净打孔放入;1公斤羊肉加咖喱粉10克;1公斤羊肉加剖开的甘蔗200克;1公斤水烧开,加羊肉1公斤、醋50克,煮沸后捞出,再重新加水加调料。
12、煮水饺时,在锅中加少许食盐,锅开时水也不外溢  
13、面条时加一小汤匙食油,面条不会沾连,并可防止面汤起泡沫、溢出锅外
14、煮面条时,在锅中加少许食盐,煮出的面条不易烂糊  
15、熬粥或煮豆时不要放碱,否则会破坏米、豆中的营养物质
16、用开水煮新笋容易熟,且松脆可口;要使笋煮后不缩小,可加几片薄荷叶或盐
17、猪肚煮熟后,切成长块,放在碗内加一些鲜汤再蒸一会儿,猪肚便会加厚一倍
18、煮猪肚时,千万不能先放盐,等煮熟后吃时再放盐,否则猪肚会缩得象牛筋一样硬
19、煮牛肉:为了使牛肉炖得快,炖得烂,加一小撮茶叶(约为泡一壶茶的量,用纱布包好)同煮,肉很快就烂且味道鲜美。
20、煮牛肉和其他韧、硬肉类以及野味禽类时,加点醋可使其软化。
21、炖老鸡:在锅内加二三十颗黄豆同炖,熟得快且味道鲜;或在杀老鸡之前,先灌给鸡一汤匙食醋,然后再杀,用文火煮炖,就会煮得烂熟;或放3~4枚山楂,鸡肉易烂
22、老鸡鸭用猛火煮,肉硬不好吃;如果先用凉水和少许食醋泡上2小时,再用微火炖,肉就会变得香嫩可口
23、炖老鸭:在锅里放几个田螺容易烂熟   
24、烧鸭子时,把鸭子尾端两侧的臊豆去掉,味道更美  
25、煮咸肉:用十几个钻有许多小孔的核桃同煮,可消除臭味
26、红烧牛肉时,加少许雪里红,肉味鲜美  
27、做红烧肉前,先用少许硼砂把肉腌一下,烧出来的肉肥而不腻,甘香可口  
28、油炸食物时,锅里放少许食盐,油不会外溅  
29、在春卷的拌馅中适量加些面粉,能避免炸制过程中馅内菜汁流出糊锅底的现象
30、炸土豆之前,先把切好的土豆片放在水里煮一会儿,使土豆皮的表面形成一层薄薄的胶质层,然后再用油炸   
31、炸猪排时,在有筋的地方割2~3个切口,炸出来的猪排就不会收缩  
32、将鸡肉先腌一会儿,封上护膜放入冰箱,待炸时再取出,炸出的鸡肉酥脆可口
33、煎荷包蛋时,在蛋黄即将凝固之际浇一点冷开水,会使蛋又黄又嫩
34、煎鸡蛋时,在平底锅放足油,油微热时蛋下锅,鸡蛋慢慢变熟,外观美,不粘锅
35、煎鸡蛋时,在热油中撒点面粉,蛋会煎得黄亮好看,油也不易溅出锅外
36、用羊油炒鸡蛋,味香无异味
37、炒鸡蛋时加入少量的砂糖,会使蛋白质变性的凝固温度上升,从而延缓了加热时间,加上砂糖具有保水性,因而可使蛋制品变得膨松柔软  
38、炒鸡蛋时加入几滴醋,炒出的蛋松软味香  
39、炒茄子时,在锅里放点醋,炒出的茄子颜色不会变黑  
40、炒土豆时加醋,可避免烧焦,又可分解土豆中的毒素,并使色、味相宜
41、炒豆芽时,先加点黄油,然后再放盐,能去掉豆腥味  
42、炒波菜时不宜加盖  43、炒肉片:肉切成薄片加酱油、黄油、淀粉,打入一个鸡蛋,拌匀,炒散;等肉片变色后,再加佐料稍炒几下,肉片味美、鲜嫩 44、炒牛肉丝:切好,用盐、糖、酒、生粉(或鸡蛋)拌一下,加上生油泡腌,30分钟后再炒,鲜嫩可口  
45、炒肉菜时放盐过早熟得慢,宜在将熟时加盐,在出锅前再加上几滴醋,鲜嫩可口 46、肉丝切好后放在小苏打溶液里浸一下再炒,特别疏松可口不论做什么糖醋菜肴,只要按2份糖1份醋的比例调配,便可做到甜酸适度  
47、炒糖醋鱼、糖醋菜帮等,应先放糖,后放盐,否则食盐的“脱水”作用会促进菜肴中蛋白质凝固而“吃”不进糖分,造成外甜里淡  
48、做肉饼和肉丸子时,一公斤肉馅放2小匙盐  
49、做丸子按50克肉10克淀粉的比例调制,成菜软嫩  
50、做滑炒肉片或辣子肉丁,按50克肉5克淀粉的比例上浆,成菜鲜嫩味美
51、做馒头时,如果在发面里揉进一小块猪油,蒸出来的馒头不仅洁白、松软,而且味香
52、蒸馒头时掺入少许桔皮丝,可使馒头增加清香  
53、蒸馒头碱放多了起黄,如在原蒸锅水里加醋2~3汤匙,再蒸10~15分钟可变白
54、将少量明矾和食盐放入清水中,把切开的生红薯浸入十几分钟,洗净后蒸煮,可防止或减轻腹胀
55、牛奶煮糊了,放点盐,冷却后味道更好56、放有辣椒的菜太辣时或炒辣椒时加点醋,辣味大减
57、烹调时,放酱油若错倒了食醋,可撒放少许小苏打,醋味即可消除
58、菜太酸,将一只松花蛋捣烂放入
59、菜太辣,放一只鸡蛋同炒
60、菜太辣,放些醋可减低辣味
61、菜太苦,滴入少许白醋
62、汤太咸又不宜兑水时,可放几块豆腐或土豆或几片蕃茄到汤中;也可将一把米或面粉用布包起来放入汤中
63、汤太腻,将少量紫菜在火上烤一下,然后撒入汤中
64、花生米用油炸熟,盛入盘中,趁热撒上少许白酒,稍凉后再撒上少许食盐,放置几天几夜都稣脆如初
65、菜籽油有一股异味,可把油烧热后投入适量生姜、蒜、葱、丁香、陈皮同炸片刻,油即可变香
66、用菜油炸一次花生米就没有怪味了,炒出的菜肴香味可口,并可做凉拌菜
67、炸完食物后的油留下一些残渣并变得混浊,可将白萝卜切成厚圆片,用筷子把萝卜戳几个洞,放入剩油中炸,残渣会附着在萝卜片上,取出清除残渣,再反复放入锅中炸,混浊的油可变清澈
68、炒菜时应先把锅烧热,再倒入食油,然后再放菜
69、当锅内温度达到最高时加入料酒,易使酒蒸发而去除食物中的腥味
70、熬猪油:在电饭褒内放一点水或植物油,然后放入猪板油或肥肉,接通电源后,能自动将油炼好,不溅油,不糊油渣,油质清纯
71、泡菜坛中放十几粒花椒或少许麦芽糖,可防止产生白花。

Sunday, April 12, 2009

Work with Latx and try to be a researcher

Yesterday, Dr. Cao, my instructor, initialized a paper review in Room 605, 25th building. Dr. Cao, and other talented and skillful students, shared much information and experiences about how to write academic papers. I think I learn much yesterday, and some inhibations, like words typeface and abbreviation manners, I even have not noticed... How luck I am...

The next step for me is learning how to write on Latex. It seems harder than Office Word and OpenOffice Writer. Although Latex is much stronger than both of them mentioned above, I trid to face it before, for its complicate grammer and the huge number of identifiers and macros. Today, the comfortable day needs to be terminated...

Friday, April 10, 2009

The first taste of tired

I always believe that I am a workaholic. During those days fulfilled with classes, I spent nearly all my leisure time in class room on textbooks and exercises. When the rehearsal in the hall was finished, I always chose the classroom as the first option, as I like the life style like this; I love to be busy......
However, today, I really felt a little tired, both mentally and physically. The system, my first design, called 'hybrid framework for action recognition’, was getting bigger and more complicated. I spent more than 24 hours to finish and debug a function, which has only little weight in the framework and is a very small segment of the system, and I had worked out 25 functions like that... no progress, no result, no graphic output... I feel tired now. An upset staff came into my mind then, IBM OS/2. OS/2 is such a system, designed by a group of talent scientists and engineers, but they spent too much resource on trivial details and failed in competition at last...
I do not want my first design dead. However, I could do nothing more but add more code on it to maintain its life... More codes, more complicated, more errors, more codes to fix it, again and again, dead loop…


ROC curve analysis: introduction

The diagnostic performance of a test, or the accuray of a test to discriminate diseased cases from normal cases is evaluated using Receiver Operating Characteristic (ROC) curve analysis (Metz, 1978; Zweig & Campbell, 1993). ROC curves can also be used to compare the diagnostic performance of two or more laboratory or diagnostic tests (Griner et al., 1981).

When you consider the results of a particular test in two populations, one population with a disease, the other population without the disease, you will rarely observe a perfect separation between the two groups. Indeed, the distribution of the test results will overlap, as shown in the following figure.

For every possible cut-off point or criterion value you select to discriminate between the two populations, there will be some cases with the disease correctly classified as positive (TP = True Positive fraction), but some cases with the disease will be classified negative (FN = False Negative fraction). On the other hand, some cases without the disease will be correctly classified as negative (TN = True Negative fraction), but some cases without the disease will be classified as positive (FP = False Positive fraction).

Schematic outcomes of a test

The different fractions (TP, FP, TN, FN) are represented in the following table.

Disease
TestPresentn Absentn Total
PositiveTrue Positive (TP)a False Positive (FP)c a + c
NegativeFalse Negative (FN)b True Negative (TN)d b + d
Total a + b c + d

The following statistics can be defined:

Sensitivity
a
a + b
Specificity
d
c + d
Positive
Likelihood
Ratio
Sensitivity
1 - Specificity
Negative
Likelihood
Ratio
1 - Sensitivity
Specificity
Positive
Predictive
Value
a
a + c
Negative
Predictive
Value
d
b + d
  • Sensitivity: probability that a test result will be positive when the disease is present (true positive rate, expressed as a percentage).
    = a / (a+b)
  • Specificity: probability that a test result will be negative when the disease is not present (true negative rate, expressed as a percentage).
    = d / (c+d)
  • Positive likelihood ratio: ratio between the probability of a positive test result given the presence of the disease and the probability of a positive test result given the absence of the disease, i.e.
    = True positive rate / False positive rate = Sensitivity / (1-Specificity)
  • Negative likelihood ratio: ratio between the probability of a negative test result given the presence of the disease and the probability of a negative test result given the absence of the disease, i.e.
    = False negative rate / True negative rate = (1-Sensitivity) / Specificity
  • Positive predictive value: probability that the disease is present when the test is positive (expressed as a percentage).
    = a / (a+c)
  • Negative predictive value: probability that the disease is not present when the test is negative (expressed as a percentage).
    = d / (b+d)

Sensitivity and specificity versus criterion value

When you select a higher criterion value, the false positive fraction will decrease with increased specificity but on the other hand the true positive fraction and sensitivity will decrease:

When you select a lower criterion value, then the true positive fraction and sensitivity will increase. On the other hand the false positive fraction will also increase, and therefore the true negative fraction and specificity will decrease.

The ROC curve

In a Receiver Operating Characteristic (ROC) curve the true positive rate (Sensitivity) is plotted in function of the false positive rate (100-Specificity) for different cut-off points. Each point on the ROC plot represents a sensitivity/specificity pair corresponding to a particular decision threshold. A test with perfect discrimination (no overlap in the two distributions) has a ROC plot that passes through the upper left corner (100% sensitivity, 100% specificity). Therefore the closer the ROC plot is to the upper left corner, the higher the overall accuracy of the test (Zweig & Campbell, 1993).

Thursday, April 9, 2009

Ground truth

Ground truth is a term used in cartography, meteorology, analysis of aerial photographs, satellite imagery and a range of other remote sensing techniques in which data are gathered at a distance. Ground truth refers to information that is collected "on location". In remote sensing, this is especially important in order to relate image data to real features and materials on the ground. The collection of ground-truth data enables calibration of remote-sensing data, and aids in the interpretation and analysis of what is being sensed.

More specifically, ground truth may refer to a process in which a pixel on a satellite image is compared to what is there in reality (at the present time) in order to verify the contents of the pixel on the image. In the case of a classified image, it allows supervised classification to help determine the accuracy of the classification performed by the remote sensing software and therefore minimize errors in the classification such as errors of commission and errors of omission.

Ground truth is usually done on site, performing surface observations and measurements of various properties of the features of the ground resolution cells that are being studied on the remotely sensed digital image. It also involves taking geographic coordinates of the ground resolution cell with GPS technology and comparing those with the coordinates of the pixel being studied provided by the remote sensing software to understand and analyze the location errors and how it may affect a particular study.

Ground truth is important in the initial supervised classification of an image. When the identity and location of land cover types are known through a combination of field work, maps, and personal experience these areas are known as training sites. The spectral characteristics of these areas are used to train the remote sensing software using decision rules for classifying the rest of the image. These decision rules such as Maximum Likelihood Classification, Parallelepiped Classification, and Minimum Distance Classification offer different techniques to classify an image. Additional ground truth sites allow the remote sensor to establish an error matrix which validates the accuracy of the classification method used. Different classification methods may have different percentages of error for a given classification project. It is important that the remote sensor chooses a classification method that works best with the number of classifications used while providing the least amount of error.

Ground truth also helps with atmospheric correction. Since images from satellites obviously have to pass through the atmosphere, they can get distorted because of absorption in the atmosphere. So ground truth can help fully identify objects in satellite photos.

(PS: All data from Wikipedia)

MEX, C++ and matlab...

I wrote the code all day long. I met a series of challenges. The program nearly killed me!!!


The first problem is MEX. MEX is an interface between C/C++ and Matlab. I have an implementation of an algorithm written in C++, then, I want to integrate it into my matlab program. So I have to modify the original cpp file and make it working well with my M-files. The input data is very confusing. In Matlab, matrix is a string of elements and every element in the matrix is arranged in a row-first pattern, that means a(2,1), not a(1,2) is the next element of a(1,1). In addition, the data type is also very complicate. In matlab, numbers are stored in "double" by default, but in C++, there is no default and you are the only one control the data type. So, I forget to change the input data from "UNIT8" in a gray-level image to "double". The hole day is full filled with exceptions and breaks... It is too bad... and upset.
fortunately, I find the problem and fix it... It works well, right?!





Sunday, April 5, 2009

Tints and shades (WIKI)

In color theory, a tint is the mixture of a color with white, which increases lightness, and a shade is the mixture of a color with black, which reduces lightness. Mixing with any neutral color, including black and white, reduces chroma or colorfulness, while the hue remains unchanged.

When mixing colored light (additive color models), the achromatic mixture of spectrally balanced red, green and blue (RGB) is always white, not gray or black. When we mix colorants, such as the pigments in paint mixtures, a color is produced which is always darker and lower in chroma, or saturation, than the parent colors. This moves the mixed color toward a neutral color―a gray or near-black. Lights are made brighter or dimmer by adjusting their brightness, or energy level; in painting, lightness is adjusted through mixture with white, black or a color's complement.

It is common among some painters to darken a paint color by adding black paint―producing colors called shades―or lighten a color by adding white―producing colors called tints. However it is not always the best way for representational painting, as an unfortunate result is for colors to also shift in hue. For instance, darkening a color by adding black can cause colors such as yellows, reds and oranges, to shift toward the greenish or bluish part of the spectrum. Lightening a color by adding white can cause a shift towards blue when mixed with reds and oranges. Another practice when darkening a color is to use its opposite, or complementary, color (e.g. purplish-red added to yellowish-green) in order to neutralize it without a shift in hue, and darken it if the additive color is darker than the parent color. When lightening a color this hue shift can be corrected with the addition of a small amount of an adjacent color to bring the hue of the mixture back in line with the parent color (e.g. adding a small amount of orange to a mixture of red and white will correct the tendency of this mixture to shift slightly towards the blue end of the spectrum).

An extension of the color wheel: the color sphere. Colors nearest the center or the poles are most achromatic. Colors of the same lightness and saturation are of the same nuance. Colors of the same hue and saturation, but of different lightness, are said to be tints and shades. Colors of the same hue and lightness, but of varying saturation, are called tones.
Some shades of blue