I need a break

my mac’s hard drive crashed!!! It happened last month but the disc utility managed to make it survive for another month. Most of the work have properly back-up using Time Machine. We bought a new hard drive for £49 – 320GB and now our macbook is back..,,!!!! so we have an empty hard drive and we are busy downloading movies for this weekend.

Hari ini update simulation biology sedikit and giving a final touch for my poster  to be printed tomorrow…


LaTeX Poster Template

Do you have any latex poster template to share?  I am preparing a poster paper for a workshop this month.  I couldn’t find any poster templates in LaTeX that I liked, and frankly I have started preparing it using open office instead. However, just wanna give it a try preparing it using latex. Found the following template form Mr. google and I think it suits my poster. I prefer to produce a `simple’ but `elegant’ type of poster and since this paper have lots of graphics rather than result, I think I would like to only have two-column instead of three.

I should remember that preparing a poster is very different from preparing a paper. You will not be enlarging your research paper and wallpapering the display board. Your main objective in preparing text for this presentation is to edit it down to very concise language. Use bullets and numbers to break text visually and aid you in the interactive use of your poster.

This is how the poster looks like ( p/s: still have way though….)..

The final version: to be updated after tomorrow meeting ok?

Meeting outcome: finalised paper to be submitted and Alan is happy to involve and go though the paper..:P..The poster needs more graphic…means another version need to be prepared…and finally finally…ICARIS deadline and all experiment should ready this week to be discussed with Jon next week.

Using Multiple X- and Y-Axes in Matlab:

I am trying to plot another graph having Double Axis Graphs for my simulation.  lets see whether it works or not. This is the output…So the analysis shall starts…

Next meeting: Wedenesday 1 p.m over in electronics

Bila dah third year

I started my third year last october and sungguh banyak kerja..meeting…presentation…conference paper …reading …writing…and deadlines…I am now have started the analysis part, even though it as an algorithm development, we still have to run some statistical tests, and last week, while finishing the conference paper ( yay…submitted to jon already!!!), managed to plot and run some statistical tests using Matlab and now I am ready to test my next algorithm and continue my algorithm analysis.

This month is another hectic month…another paper submission on 24th of March … I am starting to understand on how to write a conference paper…hopefully it works this time. So the list of task are as follow :

Week 1
  1. Conference paper
  2. Poster paper
  3. Outline paper for ICARIS 2010

(should be ready for monday meeting…)

Week 2
  1. Simulation 1
  2. Simulation 2
  3. Simulation 3
  4. Analysis
  5. Update paper
Week 3 Finalised paper and send to Jon
Week 4 Update and correct paper

Statistics for me ….

Both Quoted from Wikipedia

In descriptive statistics, the interquartile range (IQR), also called the midspread or middle fifty, is a measure of statistical dispersion, being equal to the difference between the third and first quartiles.

Unlike the (total) range, the interquartile range is a robust statistic, having a breakdown point of 25%, and is thus often preferred to the total range.

The IQR is used to build box plots, simple graphical representations of a probability distribution.

For a symmetric distribution (so the median equals the midhinge, the average of the first and third quartiles), half the IQR equals the median absolute deviation (MAD).

The median is the corresponding measure of central tendency.IQR = Q3 − Q1

In descriptive statistics, a box plot or boxplot (also known as a box-and-whisker diagram or plot) is a convenient way of graphically depicting groups of numerical data through their five-number summaries: the smallest observation (sample minimum), lower quartile (Q1), median (Q2), upper quartile (Q3), and largest observation (sample maximum). A boxplot may also indicate which observations, if any, might be considered outliers.

Boxplots display differences between populations without making any assumptions of the underlying statistical distribution: they are non-parametric. The spacings between the different parts of the box help indicate the degree of dispersion (spread) and skewness in the data, and identify outliers. Boxplots can be drawn either horizontally or vertically.

How to read a box plot: from ( http://www.helium.com/items/1275773-how-to-read-and-interpret-a-box-plot)

In statistical analysis, a box plot is a graph that can be a valuable source of easy-to-interpret information about a sample of study. A box plot can provide information about a sample’s range, median, normality of the distribution, and skew of the distribution. It can also identify and plot extreme cases within the sample.

Box and Whiskers:

The box plot shows a box encased by two outer lines known as whiskers. The box represents the middle 50% of the data sample – half of all cases are contained within it. The remaining 50% of the sample is contained within the areas between the box and the whiskers, with some exceptions (these exceptions are called outliers and they will be discussed more extensively later). For example, consider a sample of 100 IQ scores. The bottom 25% of the scores would be represented by the space between the lower whisker and the box, the middle 50% would be within the box, and the remaining 25% would be contained between the box and the upper whisker.

Box Position:

The location of the box within the whiskers can provide insight on the normality of the sample’s distribution. When the box is not centered between the whiskers, the sample may be positively or negatively skewed. If the box is shifted significantly to the low end, it is positively skewed; if the box is shifted significantly to the high end, it is negatively skewed.

Box Size:

The size of the box can provide an estimate of the kurtosis – the peakedness – of the distribution. A very thin box relative to the whiskers indicates that a very high number of cases are contained within a very small segment of the sample. This signifies a distribution with a thinner peak. A wider box relative to the whiskers indicates a wider peak. The wider the box, the more U-shaped the distribution becomes.


Outliers are not present in every box plot. When they are present, they are found in the form of points, circles, or asterisks outside of the boundaries of the whiskers. These are extreme values that deviate significantly from the rest of the sample and they can exist above or below the whiskers of the box plot.

What I’ve got today …a boxplot from 1 of the experiment…its easy to draw this using matlab – simply by boxplot([a,b,c,d,e,f,g,h,i,j,k])