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Hello,
In my experiment setup, I have different servers that host files with different sizes. The client downloads files continually and estimates the performance of each server by estimating the throughput. However, when I download a big file it shows higher throughput comparing small files.
My question, for a given server, how I can aggregate correctly the measured throughputs to have a single value that represents its performance.
Thank you very much for your time.
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One possible reason could be that many smaller files involve that mean TCP connection establishment and tear downs, while one largefile would have only one TCP establishment and tear down. So the overhead when having many small files is larger.
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dear all, i'm a newbie in ns2, and i got assigment to implement OLSR + PGB in ns2, my problem is i don't know how to get signal strength neighboar. i need "signal strength" to split into 3 area (in, PGB, out) then i can chose MPR in PGB area only. can you help me sir, please regrad
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hii...Abdulhalim,
thanks for your solution, i will try to learn ns3 first
if i move to ns3, can you help me to make may new protocol sir?
thanks
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Hi all, 
Currently, I am doing a research in classifying RBCs into four groups.
After extracting best features, I trained a pattern recognition Neural Network and performance is evaluated by 10-fold cross validation check. Miss-classification rate is around 6%. 
In another experiment, I tried to use SVM model to classify one group and three other groups so called one-versus-rest classifiers. Then again repeated same strategy for the 3-group class until there is no need for another binary classification. Performance is evaluated by the same technique applied in the Neural Network strategy. Miss-classification rate for the first binary SVM classifier is zero and for the second and third binary SVM classifier is 2.1% and 3.2%. Which strategy is better and why?
If multi stage SVM is outperforming NN strategy, how can I evaluate the miss-classification rate?
Cheers,
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If each of your examples belongs to only one of the four classes, your problem is a multi-class one, and there are many ways to solve it. SVMs can also be non-linear if using a kernel. As Niall already pointed out, there is no a "best" method for all the problems, and you need to look for understanding of the dataset, or to make empirical comparisons.
The F1 measure is a good metric of performance in multi-class problems. There are two versions of this: macro-F1 and micro-F1. However, since you have a single-label multi-class problem, the micro-F1 is the same as the accuracy. The macro-F1 measures the performance per class, and in that way, it compensates for class imbalance. This is computed as:
F1i = 2(Pi)(Ri)/(Pi+Ri)  --> Macro F1 for class i
Pi = TPi/(TPi + FPi) --> Precision for class i
Ri = TPi/(TPi + FNi) --> Recall for class i
TPi = Number of True Positive classifications for class i (positive examples classified correctly)
FPi = Number of False Positive classifications for class i (negative examples classified as positive)
FNi = Number of False Negative classification for class i (positive examples classified as negative)
After computing the different F1 for each one of the four classes, you can do a simple average to estimate the general performance of the model.
In addition, you can run a statistical test between the results obtained from two different models to see if such results are significant. One good test in classification is the McNemar’s one.
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what are the latest trends in the queuing network optimization
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read this article  and check the list of references at the end:
regards.
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In my setup, the probability density function of the noise is not Gaussian-like and its PDF changes with the system statement.
Thus, I want to use the BER calculation by error counting instead of BER estimation. Moreover, the experimental BER level is around 10^-5. However, it seems the common method to calculate the BER is based on the PDF estimation.
How can I calculate the error by direct error counting? I set up a simple system model in the attachment.
Could anyone give me some advice?
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Hello all,
actually, beside various BER estimation methods relying on assumptions about the statistical distribution of the signal characteristics (phase/amplitude) at the detector, VPItransmissionMaker Optical Systems also provides modules for estimating the BER based on error counting (noted as Monte-Carlo method).
As Mohamed pointed out above, you should make sure sufficient number of bits (or symbols if you use higher order modulation formats) is transmitted in order to count sufficient number of errors. As rough estimate, the accuracy of your estimated BER value scales with 1/sqrt(#-of-errors). So when you count 100 errors, you have an accuracy of approx. 1/sqrt(100) = 10%.
Best regards,
Andre
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my output is as in attach file
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Please provide sufficient information. It is not clear in what basis, you evaluated the results. What type of network is considered - is it wireless or wired?
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I am searching for the best network simulator to evaluate the performance of safety application through WSMP protocol in vehicular ad-hoc networks. I could only find NCTUns 6.0. I'm just wondering if there are other means to do the evaluation or other types of network simulator?
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How about veins which combines omnet++ and sumo?