2 Electrical Engineers's Projects

memories

2011-01-18T04:12:00.002+03:30

I am writing this post to remember the day bright girls were far from each other but still were working together. Remember This post!

Now we live in the same house, study in the same university, and even do research in the same lab! We are going to do common projects, co-author papers, and share difficulties of life.

We will fall apart again next year, but again we will gather together, all of us, with all our family, and share enjoy of life :)

2010-12-14T18:49:00.000+03:30

Microbial Fuel Cells (MFCs) to scavange energy

2009-11-15T09:19:00.002+03:30

Source: http://www.trophosenergy.com/
MFC Overview:

MFCs can work anywhere bacteria live. The main idea is that anywhere microbes live, electrical power is produced as a product of their metabolism. The term microbe includes both aerobic and anaerobic microorganism such as bacteria, fungi and protozoa. MFCs convert chemical energy stored in organic matter directly into electricity by using natural metabolism of those microbes. They are used in low power levels between 0.1 to 10 watts.

As an example, MFC can be used for water treatment. It converts wastewater pollutants into electricity and produces clean water as well as electricity. In fact bacteria consume pollutant and produce electrons. These electrons then flow through a circuit and create electric current. the Australian beer maker Fosters and scientists at the University of Queensland applied MFCs to generate clean energy from brewery wastewater using sugar-consuming bacteria at a brewery near Brisbane, the capital of the state of Queensland. The process yield electric power that is large enough to run the treatment plant and product clean water as byproduct.

Structure of an MFC:

It has a similar structure to a battery and contains anode, cathode and electrolyte. At the anode, fuel is metabolized by microorganisms which generate electrons and protons. Electrons are then transferred to the cathode through an external electrical circuit to be stored. However protons go to cathode via electrolyte and are combined with the oxygen and electrons of water.

Advantages:

- Clean

- Renewable

- Low cost

- Efficient (far above 50%)

- Long life(for over 7 years)

- Autonomous and stable operation

- Minimal maintenance

- Wide range of operating environments from LED lights to sea-floors

- With focus in remote applications and specially Wireless Sensor Networks in industrial and harsh areas

Trophos Energy, get energy from soil, wastewater or microbes

2009-11-15T08:29:00.001+03:30

Very interesting!

Get ENERGY from WASTE WATER, SOIL or HUMAN BODY's MICROBES!!

Trophos Energy, Inc. was founded in 2008 to research, develop and commercialize low cost, reliable remote energy harvesting systems based on Microbial Fuel Cell (MFC) technology. MFCs draw their energy from the natural metabolism of indigenous microbes, offering years of reliable, maintenance-free operation in organic-rich environments. Trophos' power management systems operate indefinitely when placed in soil, marine sediment or wastewater where solar, battery or other power sources may be technically or cost prohibitive.

Trophos leverages its proprietary technology to deliver power to wireless communication and sensor networks, offering new opportunities for military, water utility and industrial monitoring applications. Other applications include wastewater treatment and bio-remediation.

www.trophosenergy.com/

ECT100- thermal energy scavenging technology- even works in 2 centigrades diffrence

2009-10-27T22:00:00.000+03:30

Product's website: http://www.tdc.co.uk/index.php?key=ect100

This is a product from EnOcean company in Germany.

EnOcean has developed a thermal energy harvester which is able to power wireless sensor nodes from temperature differentials of only a few Kelvin.

This new energy harvester ECT100 is based on a revolutionary DC/DC converter which automatically starts operation at 20mV.

ECT100 converts an input voltage in the range of 0.02V to 0.25V to an output voltage in the range of about 4V to 4.5V. The input voltage can e.g. be generated from a temperature differential using a peltier element.

The output power depends on the actual temperature difference between both sides of the Peltier element and the element being used. It ranges from μW to mW at 3.5V. Therefore ECT100 is designed for use with EnOcean radio technology in sensors and actuators.

It’s size is around 4 cm.

A fan working with heat

2009-10-27T21:58:00.000+03:30

More info about the product click here!

Also there is a fan for stove which works without any electric sources. It just works when the stove get hot. The Vulcan Stove fan goes fast when the temperature gets higher.

How It works?

It utilizes a small, quiet Stirling cycle power plant built into the fan.The Stirling cycle power plant obtains its power from rapidly heating and cooling the same volume of air. When the air is heated, it expands, pushing a piston upward; when the same volume of air is rapidly cooled, it contracts, pulling the same piston downward, providing power. The same volume of air is heated and cooled at a very rapidly converting the heat energy to mechanical energy used to turn the fan blade.

Of course it could be used also as an electricity producer. Circulation of fan can produce electricity. But I don’t know how much size it has and if it is possible to apply such an electric source in industrial environments for each sensor node. The product’s size is about 30X30 cm.

About harvesting energy from high tempreture

2009-10-27T21:55:00.001+03:30

The technology to cleanly and quietly turn heat into electricity without the use of a turbine or generator has existed for nearly a century. The trouble is, it has never been efficient enough for widespread practical use.

I studied about Thermoelectric effect. There could be two methods two produce electricity from thermal energy.

First, by using stirling cycle power and converting thermal energy to mechanical energy(it is produced by difference in temperature and expanding and contracting. As a result a piston moves up and down and it is then converted to rotating energy) and then converting rotating energy to electrical power.

Second, by using Seebeck effect.(for more info see this article in wikipedia) The effect is that a voltage, the thermoelectric EMF, is created in the presence of a temperature difference between two different metals or semiconductors. This causes a continuous current in the conductors if they form a complete loop. The voltage created is of the order of several microvolts per kelvin difference. One such combination, copper-constantan, has a Seebeck coefficient of 41 microvolts per kelvin at room temperature. This little electricity can then be reproduced by DC/DC converters and finally be something about 3 or 4 volts. But the problem is that the ECT100(a thermal energy scavenging tech which I’ll introduce later) works with input voltage of at least several mv.

UCLA Energy Scavenging Project

2009-10-25T20:51:00.000+03:30

In this project they are trying to make extremely efficient energy harvesting. It means that they are trying to schedule energy harvesting such that the energy be used directly after harvesting and not to be stored in a battery and this is because storing in the battery adds the loss of battery storage and hence decreases efficiency.
It explains a method for efficient energy harvesting in practical distributed sensor networks and compares it with the optimum method which is not distributed and shows that the difference is not very much. There has been some publications based on this project but this project has been done on 2005 and 2006.

UCLA Energy Scavenging Project

2009-10-24T20:30:00.000+03:30

The website is: http://research.cens.ucla.edu/projects/2005/Network_Autonomy/energy_scavenging/
Next time I will be surveying on this website and another one introducing a real project on energy resolutions for wireless sensor networks from Perpetua technology.

Energy Harvesting Projects

2009-10-24T20:25:00.003+03:30

Link to the paper: Download it!

This article discusses about current energy harvesting projects but unfortunately in year 2005! :D
I read all the article through and found out various energy harvesting possibilities like solar, piezoelectric, thermal, vibration, kinetic energy and even human passive activities.(It only introduces but not explain completely)
But the important part is that it introduces “Energy Scavenging” project in UCLA. I will then go through this project’s website and explain how they make a smart network which detects potentials of environment as an energy provider and then scavenges energy.

Effective Design of WSNs: From the lab to the real world

2009-10-24T20:22:00.002+03:30

Link to the paper: Download it!

This paper discusses main characteristic of a credible WSN. For instance, energy harvesting, robustness and topology changes are the main issues in WSNs.
It introduces solar cells as energy harvesting parts in outdoor environments. As an example in marine monitoring and rock fall forecasting.
It introduces and examines previous works in this area and criticizes that a credible WSN is not only a prototype working well in laboratories and it needs to still work in real harsh environments. Also it needs to use non hazardous substances according to RoHS.
As it mainly discusses about outdoor harsh environments like bay, ocean, jungle,… and solar energy harvesting methods, I did not study it all and just had a glance.

But the main point is that I found its references very useful to research about harsh environments and energy harvesting methods. E.g. one of the references is “Energy Harvesting Projects” published by IEEE:
-See the next post-

Energy efficient secure pattern based data aggregation for wireless sensor networks

2009-10-24T20:16:00.002+03:30

Link to the paper: Download it!

This paper discusses about a data aggregation protocol to decrease redundancy and hence increase bandwidth efficiency. This protocol is called: ESPDA: Energy Efficient Secure Pattern based Data Aggregation. This protocol eliminates redundancy by making all nodes with the same data sleep except for one. It is secured since cluster-head in this method do not need to know about sensor node’s data(the cluster head is in charge of broadcasting within the cluster). Hence sensor node sends encrypted data via the cluster-head to the base station and by establishing a secure end to end communication. This is the first protocol providing both security and aggregation together.
Finally it provides simulation results with C and using GloMoSim. Working on efficient generation of pattern codes is mentioned as future work.

Q: How does it do aggregation?
A: by using pattern code. Each node gathers data from all its sensing units, Makes a pattern code and sends it to cluster head. The cluster head gets all patterns from all its nodes and decides which node to sense data and send it. Then other nodes sleep.

Q: What is a pattern code?
A: A pattern code is made based on the main ideas of observed object. E.g. if the observed object is a picture of a human, the main recognition parameters for human is considered to create pattern code.

Q: How the Cluster head determines the best pattern code?
A: It has a sense of distinct pattern code based on the object being observed.

Q: How doesn’t the cluster head need to know about data sent by sensor node?
A: It only examines pattern codes and after that does not observe any data from the determined sensor node.

Q: How sensor node encrypts data without exchanging a key with base via cluster head?
A: each node has an id and also a secret key. Base station has a database of all ids and their secret keys. And this database is made in manufacturing phase. When a node sends information it sends its id also. Hence Base can decrypt data. To have a better security and ensure freshness, base broadcasts a common key named Kb in the network. All nodes XOR their secret key with Kb and hereafter use this new key.

Q: Encrypting algorithms needs process and consumes a high amount of energy. How is it energy efficient while using encryption?
A: It employs symmetric key cryptographic algorithms to ensure energy efficiency.

Summary of paper: Which wireless technology for WSN, OCARI technology

2009-10-24T19:55:00.001+03:30

Link to paper: download here!

This paper introduces OCARI and asks for making this technology a global standard for industrial harsh environments. This method has been tested in several power plants and warships. Its advantages over previous methods which were mainly proposed by Zig Bee are deterministic MAC, energy optimization and mobility with the speed of man walking.

Application of WSNs in industrial harsh environments requires following characteristics in Physical and MAC layer:
1. robust radio transmission meaning low error regarding radio interferences. here we have a new variable named SINR= Signal to interference plus noise ratio.
2. Low power consumption
3. Compatibility with electromagnetic constraints.
4. deterministic MACs.

in Network layer:
1. Network topology flexibility
2. network scalability(for large or high density networks)
3. transparency like self configurable upon network parameters
4. support energy aware routing protocol
5. support mobility
6. authentication support

at APL layer:
1. support different profiles
2. different communication modes
3. support EDDL( electronic device description language)
4. authentication support

Existing industrial wireless communication standards
advantages of mesh networks include self-organizing and autoconfigurable topology. They also ease maintenance in industry and are cost effective. They are also suitable for CM or CBM(Condition-Base Monitoring).
disadvantage: most of them do not take into account industrial requirements.

previous Wireless standards: in this section 3 previous standards are introduced. for example
Zig Bee: for low data rate and low latency. long battery life. based on IEEE.802.15.4-2003 for PHY and MAC layers.
Zig Bee includes mechanisms for different layers. e.g. for NWK it includes routing, joining, leaving, security, ... .

After all, This is a very new technology. The first version was published on July 2009 and this version which I'm studying has been published just some days ago on October 2009 volume of IEEE.
So OCARI needs to be improved. As mentioned by authors it has some gaps specially in NTW and APL layers.

What is a harsh environment?

2009-10-24T19:50:00.000+03:30

What is a harsh environment?
 It can be human or animals body
 North or south pole
 plains and deserts
 oceans
 fire alarm
 industrial harsh areas
 power plants
 warships

However we will mainly focus on industrial harsh environments. For industrial harsh environments there is a standard technology named OCARI(Optimization of Communication for Ad hoc Reliable Industrial networks). This technology supports mesh networks and power award ad hoc routing protocols. In its application layer(APL) it's compatible with zig bee(the only working group who has commercially available products for industrial wireless networks. It was founded very soon at the end of year 2004)

In the name of God!

2009-10-24T19:17:00.003+03:30

Hi.
This post is just our first post in the area of Wireless Sensor Networks!
I ask God to help us study hard and do well in our projects, courses and presentations.
Our new life has begun recently and overcoming pressure of life sometimes is difficult.
Now Bright Girls are very far from each other but still working in the same area and still working together in contrast with the far distance of two opposite sides of the earth.
I finish this post with the name of God!

2008-11-05T18:20:00.007+03:30

We have created software simulation of our design and tested it.
These graphs show that if the calculated number is multiplied by 100, the result will be the embedding percentage:
This file has no embedding:

This one has 40 percent of embedded data:

70 percent:

100% embedded:

2008-10-22T13:22:00.001+03:30

We have designed the hardware which calculates SigmaDelta for every window of input data. The audio data is applied to the hardware sample by sample. The SigmaDelta is calculated continuously. It is not yet very clear that how SigmaDelta is analyzed to distinguish whether there is any embedded data or not.

download the visio file

To explain more about the schematic presented in a visio file, Firstly, there is a FIFO memory. It gets a sample at the top and adds its effect to the current Sigmadelta and instead sends out the effect of the earliest sample in the FIFO memory after subtracting its effect. In other words, the hardware does not calculate the sigmadelta of every window separately but it considers the difference between two consequent samples and changes the sigmadelta. It can speed up the system more efficiently.
Secondly, there is a decoder. It gets 8-bit samples and based on its value selects the 2 appropriate registers. We have 2 sets of registers. one set to save A values(the decreasing differences) and another set to save B values( the increasing differences). If the input sample is even the hardware increases its related registers and if it is odd, it decreases them. For example, if the input sample is 54 then 54-55 register and 53-54 registers should be increased. (note that if the register has been negative this change should be applied inversely).To solve the problem which occurs when registers pass zero (and since only absolute value of registers are important) we have considered a special bit called "s" for every register. Based on "s" and the LSB bit of the sample the registers are being changed.
The logic circuit related to "s" and "LSB bit" are drawn in page 2 and is available by double-clicking the "UDgen" square. In fact, UDgen decides if the counter should be counting upward or downward.
Thirdly, the A registers are added as well as the B registers. Finally sum of As are subtracted from sum of Bs and the final SigmaDelta is calculated.

2008-09-29T15:54:00.002+03:30

We designed the implementation which we explained in previous post.
It successfully recognized embedded and non-embedded files.
download the file

This m-file gets an input audio file and calculates the parameters sample by sample. If it recognizes pre-embedded samples, it will show the sample's number- for example:i=5980.

It seems that our strategy is responding correctly!

2008-09-29T11:47:00.002+03:30

We decided to start hardware implementation. This hardware works as a device to find embedded files in different places such as web. Samples are applied to the device one by one. After a specific number of samples the hardware begins to calculate some parameters like a,b and sigmadelta and then distinguishes whether the file is embedded or not. We use a special window to capture samples and calculate the parameters.
There are two different kind of windows:
1. rectangular window: in this kind we should take a limited number of samples.
2.triangular window: in this kind we can have more expanded view on the samples.

Also we should use some registers to save a,b and other parameters. Here we are going to use 4*128 registers for 'a's and 'b's(before embedding and after embedding). To explain more, for each 2 paired numbers in histogram, we use only one register-for example if sample is 10, add 1 to the register(10,11) and if sample is 11, subtract 1 from the register(10,11). The same process is done for unpaired numbers in order to figure 'b'. each register have an additional bit to show the sign.
Now we can calculate P=(b0-a0) / (b1-a1) , b1 and a1 are calculated after embedding. Then, the device, based on the amount of P, identifies if the file is embedded or not. for embedded files, it alarms.

2008-09-22T15:40:00.002+03:30

First we corrected autostat and test it again. Only in few cases when we embedded data again in an embedded file we had problem. In other wise Sigma Delta has a way to correct itself. In this way we should do embedding from %0 to %100 to a file and calculate Sigma Delta for some point in this range ,for example 11 points :%0,10,20,30,40,50,60,70,80,90,100 ,then plot a chart for these data. After that add one unit to all samples of your audio file and do like above. Therefore you have some charts that help you to distinguish whether any data embedded to your file or not and if there are any data how much percent? Yesterday we wrote a program to plot chart for original file and saw this chart that based on theory it is true.

You can get the program there.

2008-09-18T13:43:00.001+04:30

it seems that "SigmaDelta" is true for natural 8-bit mono files. I mean when a random data is embedded into such a file, SigmaDelta increases.
but the problem is that when we double-embed an already embedded audio file, in most of the cases we observe an increase in SigmaDelta! So how embedded and non-embedded files can be distinguished?
there is a mjor point to which we should pay attention, and that is an embedded file's sigmadelta is lower than 1000 but a non-embedded one is not.
maybe this strategy can be helpful:
1. check SigmaDelta strategy, if Sigmadelta(embedded) <= Sigmadelta(original) then file is not original. 2. if not: check if sigmadelta(original)>1000 then file is original.
3. if none of the aboves: the file should be examined more...but in most cases it's not original.

what's your oppinion??

2008-08-29T23:11:00.002+04:30

This program shows that probability of inaccuracy is more than 20% in "SigmaDelta" strategy.run this program in "eightbits" folder after running "getfiles".
getfiles.m
autostat.m
as I made the observation, SigmaDelta strategy declares that when we embed a random data in a non-embeded file, in most cases, Sigmadelta increases. and dominantly this strategy is based on this idea. but what happens if this idea is misleaded in 20% of its works?
This program gets an original file, calculates its sigmadelta, then embeds data and again calculates sigmadelta.should the second sigmadelta be lower than or equal with the first one, this program detecs error and shows it on the screen. at last, it calculates number of errors and percentage of error occurances in all examinee files...for me, it found 23% of errors in 94 files. is it desired?
maybe my point of view is not correct or something is misunderstood by me?

Summary

2008-08-28T11:42:00.002+04:30

We first wrote a program that finds all the 8-bit *.wav files and then wrote another program to calculate sub for pairs in each file and save the result. After that we calculated average of subs for each file. Certainly average of modified data was less than original data, so we decided to have a threshold. if average of a file is less than this threshold we'll assume that this file is embedded and classify files by their average. (Because some original files have small average and sub and in comparison with files with large sub and average may cause mistakes.)
You can see some averages in the *.excel file which is uploaded in pervious post.Now we should find suitable threshold for each pair in each class and then find out whether the file is exactly embedded or not.

2008-08-27T12:21:00.001+04:30

some changes should be done on "getfiles", it finally will be as below:

clear
files=dir(fullfile(matlabroot,'work\Data hide\eightbits\*.wav'));
sizef=size(files);
for k=1:sizef(1)
name{k}=getfield(files,{k,1},'name');
end
i=1;
for k=1:sizef(1)
fname=char(name(k))
[data fs b]=wavread(fname);
if(b==8)
eightbit{i}=fname;
i=i+1;
end
end

put "getfiles.m" and "autostat.m"(which is explained in previous post) in the same folder with your wav files. you can put all your wav files,not only 8-bit ones... "getfiles" will exclude other files itself.
so first run getfiles and then autostat... then all .mat files are now put in that folder. you can import them one by one and copy and paste into excel...
Remember that you should start from the last .mat file! :-D
but wait! I think that's better to make a multiple deminsional matrix in one time and then copy it to exel?! that must be so simple!

2008-08-27T01:55:00.002+04:30

I wrote a new program which saves all audio files's data into a folder.We can simply import data and use it later.or do an automatic statistical research in matlab.(the program is written below, pay attention to red colored lines)

i added only one more loop to our previous program.
notice that eightname is a matrix which has the name of all the files.(the same 94 8-bit files we found!) this matrix is made by "getfiles" about which I have explained in a few posts ago.

%determining input and output
%importing input and recognizing fs and bits/sample
sizef=sizef(1);
for k=1:sizef
eightname=char(eightbit(k));
[data fs b]=eval('wavread (eightname)');
sizem=size(data);
%double->integer data
data=data.*2^(b-1)+2^(b-1);
data=uint8(data);
%count
countero=zeros(256,1);
for j=0:255
for i=1:sizem(1)
if data(i)==j
countero(j+1)=countero(j+1)+1;
end
end
end
%producing random data
r=randint(sizem(1),1);
%embedding random data to original data
for i=1:sizem(1)
data(i)=bitset(data(i),1,r(i));
end
%count
counterm=zeros(256,1);
for j=0:255
for i=1:sizem(1)
if data(i)==j
counterm(j+1)=counterm(j+1)+1;
end
end
end
%getting subtract
j=0;
for i=1:2:255
j=j+1;
km(j,1)=abs(counterm(i)-counterm(i+1));
end
j=0;
for i=1:2:255
j=j+1;
ko(j,1)=abs(countero(i)-countero(i+1));
end
matname=[eightname '.mat']
save([eightname '.mat'])
k=k+1;
end

also I made an exel file of about 10 columns. I calculated average of subs and average of every row in exel. I think we can find some useful points in average of every column. take a look on the file please.
see that average of data has decreased noticeably in modified files.
and maybe, as parisa said before, we can consider every row's average as the thereshold in order to discreminate pairing from non-pairing rows. then we will count number of positive answers. we expect to have considerably more positive answers in modified audio files.

2008-08-21T20:37:00.001+04:30

I devised the following program and used it in order to find all 8bit wav files in my computer.

clear
files=dir(fullfile(matlabroot,'work\Data hide\audio\*.wav'));
size=size(files);
for k=1:size(1)
name{k}=getfield(files,{k,1},'name');
end
i=1;
for k=1:size(1)
[data fs b]=wavread(name(k));
if(b==8)
eightbit{i}=name(k);
i=i+1;
end
end

notice that you should put all wav files in a folder and give its path to this program(line2) and then run it. you can see the name of 8bit files in matrix "eightbit". if it caused any errors first of all make sure that none of your files are compressed.
by means of this program I found 93 8bit wav files. now I'm going to download final program of last session and run it on all these files and see pairing phenomenon. but since this program doesn't matter whether the files are mono or stereo I wondered if the files should be mono or not...
I'm really eager to see pairing!!

2008-08-20T14:17:00.001+04:30

here is a way which we can record audio in Matlab and use it for our statistical research:

micrecorder = audiorecorder(11025,8,1);
record(micrecorder,2);
% Now, speak into microphone
stop(micrecorder);
speechplayer = play(micrecorder);
% Now, listen to the recording
stop(speechplayer);
data = getaudiodata(micrecorder);

this program records a mono(1 channle) 8bit audio with 11025 samples then plays it back and saves it into "data" to be used in Matlab.I adjusted the program to record only 2 seconds, scince this function is not intended for long recording otherwise it will cause lack of memorgy and Matlab performance may degrade.

another way is using "wavrecord":

y= wavrecord(2*11025,11025,'int8');
wavplay(y,11025);

Both.seseion8

2008-08-20T08:57:00.005+04:30

We correct our program and Finally could see pairing in histogram.

Changs in program are as follow:

multiple daata by 2^(b-1) nor 2^b; beacuse data is between (-1 & 1) nor( -.5 &.5) so if we multiple by 2^b e have only even numbers,

to have possitive data we add a constant num (2^(b-1)) and after embedding minus data from that constant,

use hist() structure for plotting histogram,

correct "bitset(data(i),1,r(i));" to "data(i)=bitset(data(i),1,r(i)); "!!!

correct "eval('wavwrite(data,size(1),o)');" to "eval('wavwrite(data,fs,b,o)');" (if we don't give b as NBITS ,take NBITS=16 by defualt and can cause problem in *.wav file.) .

you can download program here.

Also you can see the histograms bellow:

To do list for next session:

Write a program for extracting data.

Both.Session7

2008-08-14T16:15:00.005+04:30

Finally We used "input" MATLAB function to determine the input file and also name of the new output file.
So by means of this program it's really easy to hide a random data to a large number of audio files and investigate about changes and the "pairing" phenomenon.

also our new program can detect number of samples, bits per sample and other information needed. First It's necessary to find "bits per sample" of our audio file and then hide our data in the LSB bits of every sample,then our program continues to hide data in LSB bits appropriately and at last plots histogram of modified data.

as we have mentioned in our previous post, "setbit" cannot be used for "negative numbers". so we have to change negatives to positives and then hide data. in order to solve this problem we added all negative numbers with 2^bits(bits equals to bits per sample).
with this strategy all negative numbers change to their 2's complements and become positive. after hiding data the program changes all positive numbers greater than 2^(bits-1) back to their negative form.

primarily we tested a few files and it seems that the program is working correctly. the audio file which has a hidden data is not apparently different from the original file.(but I believe that a file named "ringin" has a noticeable change but Parisa doesn't agree with me!)

you can see the program below or download it here.

clear
in=input('type name of the wav file:','s');
o=input('type an output name:','s');
[data fs b]=eval('wavread (in)');
size=size(data);
data=data*2^b;
for i=1:size(1)
if data(i)<0 r="randint(size(1),1);" i="1:size(1)" i="1:size(1)">(2^(b-1)-1)
data(i)=data(i)-2^b;
end
end
data=data*(1/2^b);
eval('wavwrite(data,size(1),o)');

To do list for next session:

testing lots of audio files and finding out if they make a recognizable difference when they are modified or not.
find out if there is pairing in histogram of modified audio files
paying attention to stereo files with 2column matrices

Both.Session6

2008-08-10T10:13:00.005+04:30

We decided to make modified file by ourself because our goal is putting data in every 8bits ( but we didn’t have enough information about that program which is explained in last session ).

To hide data we wrote in this program ( in an M-file ):

data=wavread ('1');
data=data*256*256;
data=uint16(data);
r=randint(22046*2,1);
for i=1:22046
bitset(data(i),1,r(i));
bitset(data(i),9,r(22046+i));
end
data=double(data)
data=data*(1/(256*256));
wavwrite(data,22046,'2');

 Certainly this program has some problems that we should solve them.

For hiding data , first we should read a *.wav file and show it 8bits -8bits.now we use wavread(). This instruction gives some values(vectors): vector of data, vector (or amount ) of the bits using for exhibiting each of data and the frequency of sampling .( we use only from data because we have other value but if we have an input with unknown parameters we have to use other values ).

We have a problem in this step,because in the vector of data we have 16bits data but we need 8bits data . now we continue without paying attention to this problem .
Then we multiple data vector by 256*256 to have signed num instead of double num . after that, we made a random vector, that the 0 and 1 have same probablity , as our bit string with rand(m,n) that m=frequency of sampling & n=1 . in next step , by using a loop we hide data in original data vector . in this loop we use from bitset() to change less value bit in every 8bit to our bit string .we should attend to 2 points:

Because we have 16 bits in each row of data vector ,so we should have 2*Fs numbers ,
In bitset() we couldn’t use negative num , we have to change our data to unsigned format .

Now we have a modified data but for having a vector like original one we do as follow:
First change format to double . then multiple data by1/(256*256) .after that make *.wav file by wavwrite() ( to use wavwrite() you should give correct frequency ).we listen to this file .it is similar to original but because of deleting negative num its quality wasn’t very good.

To do list for next session :

Find out how we can read data 8bit-8bit ,
Editing our program to get input ,
Solve the problem of setbit ( there are two suggestion :first sum data with a constant number and after change minus data from that constant num ,second use from two’s complement),
Plot histogram and find the possible changes.

F.Session5

2008-08-08T23:10:00.001+04:30

Today I found the "C# data hide" program which I explained last session. It can produce a modified file from an original *.wav file. It uses a key file to hide the text embedded to the file. Also it can extract the cryptic text and the original file from a modified one. Of course the key file is needed to do so.
A view of this software is provided below:

We may use someone else's modified files in a large amount to do a statistical research or maybe we'll use above program to create lots of hidden data audio files.
I also studied 2 sections of MATLAB-jahad:
Section5.charachters and strings in MATLAB
Section6.loops and if-else instructions

To do list for the next session:
1.studying MATLAB-jahad from section7
2.don't forget about thinking if making all the numbers positive is correct or not.
3.do the statistical research

both.Session4

2008-08-04T15:22:00.002+04:30

In this session we find a useful software named:"hex editor" . you can easily find this software by searching internet. There're lots of free downloads available. In this software we can open "wav" files as "hex" and view the file's bytes and also modify them.

But we should notice that it's not possible to modify every byte we like. Some special bytes are essential for a wav file.

We've studied wav files more carefully. Below is the main explanation for the format of a wav file:( source:http://www.codeproject.com/KB/security/steganodotnet8.aspx )

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The Wave File Format

Have you ever looked at a Wave file in a HEX editor? It starts like that, and continues with unreadable binary data:

Every RIFF file starts with the text "RIFF", followed by the Int32 length of the entire file:

The next fields say that this RIFF file contains Wave data and open the format chunk:

The length of the following format chunk must be 16 for PCM files:

Now the format is being specified by a WAVEFORMATEX structure:

The format chunk can be followed by some extra information. Then the interesting parts begin with the data chunk.

The data chunk contains all the Wave samples. That means the rest of the file is pure audio data. Little changes might be hearable, but won't destroy the file.

------------------------------------------------------------------------------------------------

Also in this article there's a C# program which can embed esoteric data to a wav file, but the program's download link didn't work.

Another way to create *.wav file from variables is in MATLAB by this instruction:

wavwrite(u,Fs,bits,'filename')

But when using this instruction we should pay attention to give a correct input variable. For example its numbers should be between 1 & -1…

We began to use "stem(data)" instead of "hist(data)" because "hist" only plots some special numbers but "stem" can show all the numbers between 0 and 256 one by one.

Also since MATLAB use to divide numbers by 256, we decided to multiple floating point numbers by 256 instead of 1000. then we add numbers with 100 to have all the numbers positive. After that we count numbers by using two intricate loops. We did all these works in an m-file program which you can see below:

counter=zeros(256,1);

new=pahang*256;

new=round(new);

new=new+100;

for i=0:256

for j=1:22046

if (new(j)==i)

counter(i+1)=counter(i+1)+1;

end

i=[0:255];

figure;stem(counter)

On the pictures below you can see how "stem" can plot a better histogram of our data.

In Next step, we modified a *.wav file by "hex editor" and then imported it into MATLAB and ran above m-file program on it. Since our changes were'nt very much we couldn't find any critical difference between their histograms. When we looked for any changes in their vectors we made this observation that only first arrays have been changed a little. You can see numbers in the pictures below. First column shows modified audio file vector and second column shows the original one. The third column shows whether they are different or not.( if different 0 and if equl 1 is showed.)

To do list for next session:

Making a large number of modified files and doing a statistical research on them and find the difference between their histogram and the original file's.
continue studying matlab-jahad
Search about this point that whether making all the numbers positive is correct or not.

F.Session3

2008-08-03T20:36:00.004+04:30

In this session I've studied sections 3 and 4 of MATLAB-jahad. Main ideas in these sections are explained below: f
1. Solving a series of equations by matrices in MATLAB: We can solve equations easily by two matrices in MATLAB, one is coefficients matrix and another is the known numbers (numbers on the right side of every equation). Even we can solve equations which number of their unknown variables is more than number of equations.
2. .introducing matrix functions: like det(determinant), inverse or sudo-inverse,...
3. logic operations and their related functions: like and,or,xor,not and also you will learn how to create matrieces which are results of comparing other matrices.

Also in this session I tried to obey Parisa's suggestion. She suggested finding a way for exporting modified variables to audio files. She believes that there should be something like this in MATLAB, but till now I haven't find anything. I found "export" in "signal processing tool" and also "save as" in both "Signal processing tool" and "File menu", but they didn't work.
While I was searching for exporting data I found out "Signal processing tool" which is accessible from "Start" menu. This tool seems really powerful. We can import MATLAB variables into it. Then it will plot the variable and also its spectrum. I think another useful option is the ability to play a sound related to the variable.
As I said in previous session (my previous post), I modified the original data and converted it to a vector of 0-800. Now I imported original data and also modified one into the "signal processing tool" and compared them. I noticed that there's no critical difference between them. The only difference is that modified data has more energy that the original one. In fact you'll hear it with a higher volume.
So at this moment I'm thinking about 2 main ideas:
1. Finding a way (ex. a C program) which can separate bytes of an audio file and easily modify and then save it.
2. modify data in matlab and change every number of data's vector base on esoteric data and then bring it back to its own energy.(by minusing(?) 412 and dividing by 1000) and then compare it with original data by the means of "Signal processing tool" in MATLAB.
this picture shows this powerful tool:

To do list for next session:
1. thinking about 2 statements above
2. continue studying MATLAB-jahad from section5

F.Session2

2008-08-01T19:09:00.011+04:30

In my opinion the m-file which Sahar sent to me was not useful. You can download the file.
Section2 of MATLAB-Jahad was all about matrices, how to create them in a short way, algebraic and mathematic operations with matrices, deleting or choosing a special part of matrix or find arrays of matrix ,ex:find(a>5) will return all the arrays greater than 5 from matrix a.
I've designed an m-file(see it below) which converts double floating numbers to integers between 0-800, but when I plotted the histogram it wasn't very good, I think!

m-file:
data*1000;
ans+412;
round(ans);
hist(ans);
You can see the results in pictures below:(1.plot(ans) 2.hist(ans))

1.

2.

To do list for next session:
Continue studying MATLAB from section3.
Think on writing an m-file which can plot a better histogram.

Recognizing audio files containing hidden data, using FPGAs

2008-08-01T00:21:00.002+04:30

Project outline: That might be strange if you hear that the music file which you enjoy listening to may contain esoteric data! As an example we believe that if someone substitutes every bit of his esoteric data with LSB bits of every byte in audio file, albeit the file has changed but human's ear cannot recognize the difference made by this substitution.
In this way it's possible to hide a secret data in a common music file and exchange it via an unsecure channel. Any auditor in the channel usually cannot even guess that there is a secret data in the file.

History of this strategy: previous similar works are mostly in the field of image processing. In fact this investigation is based on similar works with images.
At this point we hope that by plotting histogram of audio file we'll be able to distinguish modified file from an unmodified original one. We believe that there's a correlation between columns in histogram of original file but it's not applied to a modified one.
Our final goal: at last we hope that we can design an Fpga based system which can recognize audio files containing hidden data.

Project explanation:
At first we tried to use MATLAB and its signal processing tools to analyze modified and unmodified files. We should do a statistical research to finally find the main point of difference and apply it to our system.
The first step is finding bytes which has made the original audio file and plot it's histogram.
One way is to using "import data" in MATLAB. But we should notice that the default format of numbers in MATLAB is "double floating". And it imports audio file to a 1 column (if mono) or 2 columns (if stereo) matrix. The numbers are between 1 and -1 and in double format. But these kinds of numbers aren't suitable to plot a histogram. So we should invent another special way to create a vector of suitable numbers. Maybe that will be suitable to multiple all the numbers by 1000 and then round them simply by applying "data=round(data)" in MATLAB.Then we can plot histogram by this instruction:
hist(data)
In this point that became clear that we should have more specialized information about MATLAB and its "signal processing tools", so in next section we will make you more familiar with Signal processing in MATLAB.

Main primary points about MATLAB:
You can save all the variables currently in the workspace and load them the next time you need them by "save" and "load" commands.
Notice that "format" command only affects output display formats of numbers and does not affect how MATLAB computations are done.
You can change format of numbers using this command:
>> a=uint8(a);

To do list for next session:

Continue studying MATLAB-jahad section2
The file sent by Sahar which shows bytes of audio files

Wednesday 2008/31/7