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  • The Fantastic 4 Meets the Moleman, Parts 1 and 2 [Classic Radio Show from 1975]

    - by Asian Angel
    Are you ready for a Marvel super hero blast from the past? Then sit back and get ready to enjoy twenty-two minutes of classic radio show goodness from 1975 as the Fantastic 4 meets the Moleman! Special Note: Bill Murray plays the part of the Human Torch in this two part episode. You can enjoy more of these classic Fantastic 4 radio episodes by visiting the videos search query page linked below: Fantastic 4 Radio Shows – MrWaltherppk1 [YouTube] HTG Explains: What Is Windows RT and What Does It Mean To Me? HTG Explains: How Windows 8′s Secure Boot Feature Works & What It Means for Linux Hack Your Kindle for Easy Font Customization

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  • Displaying Fourier transforms in OpenCV

    - by Simonw
    Hi, I'm just learning to use OpenCV and am having a problem with using DFT. I've done a signal processing class which used MatLab, so I'm trying to go through some of the exercises we did in that class. I'm trying to get and display the FT of an image, so I can mask some of the frequencies. I'd like to be able to see the FT, so I know how big to make the mask, but when I tried, I got an image like this: rather than like one of these Am I forgetting a step somewhere? I'm loading the image, converting its type to CV_32FC1, getting the matrix of it, getting the DFT, and then getting turning the resulting matrix back into an image. I'll post the code I'm using if it will be of any help? Or if someone has a link to an example of displaying the FT? I could only find ones which used it for the convolution. EDIT: Did I get the Phase of the image?

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  • Simple in-place discrete fourier transform ( DFT )

    - by Adam
    I'm writing a very simple in-place DFT. I am using the formula shown here: http://en.wikipedia.org/wiki/Discrete_Fourier_transform#Definition along with Euler's formula to avoid having to use a complex number class just for this. So far I have this: private void fft(double[] data) { double[] real = new double[256]; double[] imag = new double[256]; double pi_div_128 = -1 * Math.PI / 128; for (int k = 0; k < 256; k++) { for (int n = 0; n < 256; n++) { real[k] += data[k] * Math.Cos(pi_div_128 * k * n); imag[k] += data[k] * Math.Sin(pi_div_128 * k * n); } data[k] = Math.Sqrt(real[k] * real[k] + imag[k] * imag[k]); } } But the Math.Cos and Math.Sin terms eventually go both positive and negative, so as I'm adding those terms multiplied with data[k], they cancel out and I just get some obscenely small value. I see how it is happening, but I can't make sense of how my code is perhaps mis-representing the mathematics. Any help is appreciated. FYI, I do have to write my own, I realize I can get off-the shelf FFT's.

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  • spike in my inverse fourier transform

    - by Jon
    I am trying to compare two data sets in MATLAB. To do this I need to filter the data sets by Fourier transforming the data, filtering it and then inverse Fourier transforming it. When I inverse Fourier transform the data however I get a spike at either end of the red data set (picture shows the first spike), it should be close to zero at the start, like the blue line. I am comparing many data sets and this only happens occasionally. I have three questions about this phenomenon. First, what may be causing it, secondly, how can I remedy it, and third, will it affect the data further along the time series or just at the beginning and end of the time series as it appears to from the picture. Any help would be great thanks.

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  • DSP - Problems using the inverse Fast Fourier Transform

    - by Trap
    I've been playing around a little with the Exocortex implementation of the FFT, but I'm having some problems. First, after calculating the inverse FFT of an unchanged frequency spectrum obtained by a previous forward FFT, one would expect to get the original signal back, but this is not the case. I had to figure out that I needed to scale the FFT output to about 1 / fftLength to get the amplitudes ok. Why is this? Second, whenever I modify the amplitudes of the frequency bins before calling the iFFT the signal gets distorted at low frequencies. However, this does not happen if I attenuate all the bins by the same factor. Let me put a very simplified example of the output buffer of a 4-sample FFT: // Bin 0 (DC) FFTOut[0] = 0.0000610351563 FFTOut[1] = 0.0 // Bin 1 FFTOut[2] = 0.000331878662 FFTOut[3] = 0.000629425049 // Central bin FFTOut[4] = -0.0000381469727 FFTOut[5] = 0.0 // Bin 3, this is a negative frequency bin. FFTOut[6] = 0.000331878662 FFTOut[7] = -0.000629425049 The output is composed of pairs of floats, each representing the real and imaginay parts of a single bin. So, bin 0 (array indexes 0, 1) would represent the real and imaginary parts of the DC frequency. As you can see, bins 1 and 3 both have the same values, (except for the sign of the Im part), so I guess these are the negative frequency values, and finally indexes (4, 5) would be the central frequency bin. To attenuate the frequency bin 1 this is what I do: // Attenuate the 'positive' bin FFTOut[2] *= 0.5; FFTOut[3] *= 0.5; // Attenuate its corresponding negative bin. FFTOut[6] *= 0.5; FFTOut[7] *= 0.5; For the actual tests I'm using a 1024-length FFT and I always provide all the samples so no 0-padding is needed. // Attenuate var halfSize = fftWindowLength / 2; float leftFreq = 0f; float rightFreq = 22050f; for( var c = 1; c < halfSize; c++ ) { var freq = c * (44100d / halfSize); // Calc. positive and negative frequency locations. var k = c * 2; var nk = (fftWindowLength - c) * 2; // This kind of attenuation corresponds to a high-pass filter. // The attenuation at the transition band is linearly applied, could // this be the cause of the distortion of low frequencies? var attn = (freq < leftFreq) ? 0 : (freq < rightFreq) ? ((freq - leftFreq) / (rightFreq - leftFreq)) : 1; mFFTOut[ k ] *= (float)attn; mFFTOut[ k + 1 ] *= (float)attn; mFFTOut[ nk ] *= (float)attn; mFFTOut[ nk + 1 ] *= (float)attn; } Obviously I'm doing something wrong but can't figure out what or where.

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  • Windows Azure Training Kit (November 2010 Release Update)&ndash;Fantastic Azure training resource

    - by Jim Duffy
    At PDC 2010 in October Microsoft announced a number of new enhancements/features for Windows Azure. In case you missed it, these new enhancements/features have been released in the new Windows Azure Tools for Visual Studio November release (v1.3). The Windows Azure team blog is an excellent resource for information about the new release. Along with the new release the Azure team has also updated the Windows Azure Platform Training Kit. What is the Windows Azure Platform Training Kit you ask? It is a comprehensive set of hands-on training labs and videos designed to help you quickly get up to speed with Windows Azure, SQL Azure, and the Windows Azure AppFabric. The training kit contains updated labs including a couple I would suggest you hit first. Introduction to Windows Azure - updated to use the new Windows Azure platform Portal Introduction to SQL Azure - updated to use the new Windows Azure platform Portal The training kit contains a number of new labs as well including: Advanced Web and Worker Role – shows how to use admin mode and startup tasks Connecting Apps With Windows Azure Connect – shows how to use Project Sydney Virtual Machine Role – shows how to get started with VM Role by creating and deploying a VHD Windows Azure CDN – simple introduction to the CDN Introduction to the Windows Azure AppFabric Service Bus Futures – shows how to use the new Service Bus features in the AppFabric labs environment Building Windows Azure Apps with Caching Service – shows how to use the new Windows Azure AppFabric Caching service Introduction to the AppFabric Access Control Service V2 – shows how to build a simple web application that supports multiple identity providers Ok, that’s enough reading, go start learning! Have a day.

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  • Fantastic Rankings With Press Release Distribution

    The website of any business solution is the face of that business in the online world and so has to be made with the best techniques to ensure that clients go for the business solution on the basis of the website. There are many tools and tactics that can be utilized in the area of Search engine optimization to ensure that the web traffic comes to your website.

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  • Fourier transform software

    - by CFP
    Hello everyone! After spending a lot of time searching for this, I thought that some SuperUser gurus might know the answer :) I'm searching for an open source application to compute an FFT, that could: * Import a list of points from a text file (in any format, I could write conversion scripts if needed), for example 0,1; 1,2; 4,5 * Compute the associated discrete transform, and output the list of coefficients Ideally, it would also display the plot and the associated fourier decomposition on the same graph, to allow comparison, but this is not absolutely needed. It can be either on Windows or on Linux/Unix. Can you think of a solution? Thanks, CFP.

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  • help with matlab and Discrete Fourier transform

    - by user504363
    Hi all I have previous experience with Matlab, but the problem that I face some problems in apply a problem in (DSP : Digital signal processing) which is not my study field, but I must finish that problems in days to complete my project. all i want is help me with method and steps of solving this problem in matlab and then I can write the code with myself. the problem is about the signal x(t) = exp(-a*t); 1) what's the Discrete Fourier transform of the sampled signal with sample rate fs 2) if a=1 and fs =1 , plot the amplitude spectrum of sampled signal 3) fix the sampling frequency at fs = 1(hz) [what's it mean ?] and plot the magnitude of the Fourier Transform of the sampled signal at various values of a thanks

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  • fourier transform to transpose key of a wav file

    - by tbischel
    I want to write an app to transpose the key a wav file plays in (for fun, I know there are apps that already do this)... my main understanding of how this might be accomplished is to 1) chop the audio file into very small blocks (say 1/10 a second) 2) run an FFT on each block 3) phase shift the frequency space up or down depending on what key I want 4) use an inverse FFT to return each block to the time domain 5) glue all the blocks together But now I'm wondering if the transformed blocks would no longer be continuous when I try to glue them back together. Are there ideas how I should do this to guarantee continuity, or am I just worrying about nothing?

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  • How to extract semi-precise frequencies from a WAV file using Fourier Transforms

    - by Seisatsu
    Let us say that I have a WAV file. In this file, is a series of sine tones at precise 1 second intervals. I want to use the FFTW library to extract these tones in sequence. Is this particularly hard to do? How would I go about this? Also, what is the bast way to write tones of this kind into a WAV file? I assume I would only need a simple audio library for the output. My language of choice is C

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  • Using GNU Octave FFT functions

    - by CFP
    Hello world! I'm playing with octave's fft functions, and I can't really figure out how to scale their output: I use the following (very short) code to approximate a function: function y = f(x) y = x .^ 2; endfunction; X=[-4096:4095]/64; Y = f(X); # plot(X, Y); F = fft(Y); S = [0:2047]/2048; function points = approximate(input, count) size = size(input)(2); fourier = [fft(input)(1:count) zeros(1, size-count)]; points = ifft(fourier); endfunction; Y = f(X); plot(X, Y, X, approximate(Y, 10)); Basically, what it does is take a function, compute the image of an interval, fft-it, then keep a few harmonics, and ifft the result. Yet I get a plot that is vertically compressed (the vertical scale of the output is wrong). Any ideas? Thanks!

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  • Pitch detection and change java

    - by omegas27
    Hello, I'm french so I'm sorry if you have trouble to understand some of my sentences. Aniways, I saw in some topics that the pitch could be fetected thanks to the Fourier transform but I didn't really understand how to implement it. Moreover, I didn't find how to change the pitch of a wav file and if possibl ,a mp3 file I am listening to music using javaSound for the wav and JLayer for the mp3. Thanks

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  • convolution in R

    - by user236215
    I tried to do convolution in R directly and using FFTs then taking inverse. But it seems from simple observation it is not correct. Look at this example: # DIRECTLY > x2$xt [1] 24.610 24.605 24.610 24.605 24.610 > h2$xt [1] 0.003891051 0.003875910 0.003860829 0.003845806 0.003830842 > convolve(h2$xt,x2$xt) [1] 0.4750436 0.4750438 0.4750435 0.4750437 0.4750435 # USING INVERSE FOURIER TRANSFORM > f=fft(fft(h2$xt)*fft(x2$xt), inv=TRUE) > Re(f)/length(f) [1] 0.4750438 0.4750435 0.4750437 0.4750435 0.4750436 > Lets take the index 0. At 0, the convolution should simply be the last value of x2$xt (24.610) multiplied by first value of h2$xt (0.003891051) which should give convolution at index 0 = 24.610*0.003891051 = 0.09575877 which is way off from 0.4750436. Am I doing something wrong? Why is the values so different from expected?

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  • YOUR FREE, EXCLUSIVE, ONLINE UPDATE ON FANTASTIC NEW ORACLE PARTNER OPPORTUNITIES - REGISTER TODAY!

    - by Claudia Costa
    New products. New specializations. New opportunities.There really has never been a better time to be an Oracle partner! Find out exactly what Oracle's "Software. Hardware. Complete" strategy, and the latest developments in the OPN Specialized program, mean for your business.   Register now for the Oracle PartnerNetwork Days Virtual Event on the 29th of June at 11:00h to learn: How to use Oracle's uniquely comprehensive technology stack to grow your business How specialization with Oracle can significantly improve your competitive position How the Oracle PartnerNetwork is evolving to help you succeed Highlights include important updates from Oracle EMEA strategy, partner and product leaders, a live link to the Oracle FY11 Global Partner Kickoff, and interviews with local Oracle partners that are already enjoying the benefits of specialization. The event will also feature: ·         Live Q&A sessions with our speakers, ·         Virtual information booths packed with useful information ·         Opportunities to network with Oracle experts and your peers. ·         Special guest speaker is a former Microsoft executive who has used the principles of specialization with spectacular results to become one of the world's most successful social entrepreneurs. Plus, at the end of the event, you can submit your feedback form for your chance to win two passes to Oracle OpenWorld in San Francisco this September! CLICK HERE TO REGISTER NOW!

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  • The Fantastic New WebLogic on Oracle Database Appliance 2.9 Release is Here!

    - by JuergenKress
    Last week was a big day in virtualised ODA-land as it saw the launch of WebLogic on ODA 2.9. Admittedly it doesn't sound like a very exciting release but it is one that we at O-box have been looking forward to for quite some time. Let me explain why, then we'll look into the details... The ODA X4-2 has 48 Intel Xeon cores. That is a lot of compute power. Whilst the largest O-box SOA Appliance single environment configuration can in theory use all those cores (currently with 40 vCPU of SOA!) the vast majority of O-box users will want smaller configurations. Prior to 2.9 the Oracle WebLogic implementation only supported one domain per ODA, so the conundrum O-box development faced last year was either: offer customers only one SOA environment on their O-box for now (but have the benefit of a standard, easily supportable WebLogic installation), or build our own WebLogic/OTD OVM templates from scratch. One of our driving goals with O-box is to give the best possible experience and make the appliance as supportable as possible. Therefore we took the gamble that we would stick with the Oracle's one-domain WebLogic configuration initially, and just hope that it would deliver multi-domain support for us in a timely manner (note: this is probably not a strategy that business textbooks would recommend!). Anyway, we've been working closely with Oracle Product Management for a few months now and I'm delighted to see 2.9 as the fruits of their labour. This also neatly ties in with several recent requests for O-box to include OSB as well as SOA/BPEL (which we have always wanted to have in separate domains). The diagram below is the neatest way to summarise what the new 2.9 release will allow us to deliver, i.e. previously only one 3D box was possible: Read the complete article here. WebLogic Partner Community For regular information become a member in the WebLogic Partner Community please visit: http://www.oracle.com/partners/goto/wls-emea ( OPN account required). If you need support with your account please contact the Oracle Partner Business Center. Blog Twitter LinkedIn Mix Forum Wiki Technorati Tags: oBox,WebLogic on ODA,ODA,WebLogic,WebLogic Community,Oracle,OPN,Jürgen Kress

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  • How to generate a lower frequency version of a signal in Matlab?

    - by estourodepilha.com
    With a sine input, I tried to modify it's frequency cutting some lower frequencies in the spectrum, shifting the main frequency towards zero. As the signal is not fftshifted I tried to do that by eliminating some samples at the begin and at the end of the fft vector: interval = 1; samplingFrequency = 44100; signalFrequency = 440; sampleDuration = 1 / samplingFrequency; timespan = 1 : sampleDuration : (1 + interval); original = sin(2 * pi * signalFrequency * timespan); fourierTransform = fft(original); frequencyCut = 10; %% Hertz frequencyCut = floor(frequencyCut * (length(pattern) / samplingFrequency) / 4); %% Samples maxFrequency = length(fourierTransform) - (2 * frequencyCut); signal = ifft(fourierTransform(frequencyCut + 1:maxFrequency), 'symmetric'); But it didn't work as expected. I also tried to remove the center part of the spectrum, but it wielded a higher frequency sine wave too. How to make it right?

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  • DSP - Filtering in the frequency domain via FFT

    - by Trap
    I've been playing around a little with the Exocortex implementation of the FFT, but I'm having some problems. Whenever I modify the amplitudes of the frequency bins before calling the iFFT the resulting signal contains some clicks and pops, especially when low frequencies are present in the signal (like drums or basses). However, this does not happen if I attenuate all the bins by the same factor. Let me put an example of the output buffer of a 4-sample FFT: // Bin 0 (DC) FFTOut[0] = 0.0000610351563 FFTOut[1] = 0.0 // Bin 1 FFTOut[2] = 0.000331878662 FFTOut[3] = 0.000629425049 // Bin 2 FFTOut[4] = -0.0000381469727 FFTOut[5] = 0.0 // Bin 3, this is the first and only negative frequency bin. FFTOut[6] = 0.000331878662 FFTOut[7] = -0.000629425049 The output is composed of pairs of floats, each representing the real and imaginay parts of a single bin. So, bin 0 (array indexes 0, 1) would represent the real and imaginary parts of the DC frequency. As you can see, bins 1 and 3 both have the same values, (except for the sign of the Im part), so I guess bin 3 is the first negative frequency, and finally indexes (4, 5) would be the last positive frequency bin. Then to attenuate the frequency bin 1 this is what I do: // Attenuate the 'positive' bin FFTOut[2] *= 0.5; FFTOut[3] *= 0.5; // Attenuate its corresponding negative bin. FFTOut[6] *= 0.5; FFTOut[7] *= 0.5; For the actual tests I'm using a 1024-length FFT and I always provide all the samples so no 0-padding is needed. // Attenuate var halfSize = fftWindowLength / 2; float leftFreq = 0f; float rightFreq = 22050f; for( var c = 1; c < halfSize; c++ ) { var freq = c * (44100d / halfSize); // Calc. positive and negative frequency indexes. var k = c * 2; var nk = (fftWindowLength - c) * 2; // This kind of attenuation corresponds to a high-pass filter. // The attenuation at the transition band is linearly applied, could // this be the cause of the distortion of low frequencies? var attn = (freq < leftFreq) ? 0 : (freq < rightFreq) ? ((freq - leftFreq) / (rightFreq - leftFreq)) : 1; // Attenuate positive and negative bins. mFFTOut[ k ] *= (float)attn; mFFTOut[ k + 1 ] *= (float)attn; mFFTOut[ nk ] *= (float)attn; mFFTOut[ nk + 1 ] *= (float)attn; } Obviously I'm doing something wrong but can't figure out what. I don't want to use the FFT output as a means to generate a set of FIR coefficients since I'm trying to implement a very basic dynamic equalizer. What's the correct way to filter in the frequency domain? what I'm missing? Thanks in advance.

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  • Difficulty creating classes and arrays of those classes C#

    - by Lucifer Fayte
    I'm trying to implement a Discrete Fourier Transformation algorithm for a project I'm doing in school. But creating a class is seeming to be difficult(which it shouldn't be). I'm using Visual Studio 2012. Basically I need a class called Complex to store the two values I get from a DFT; The real portion and the imaginary portion. This is what I have so far for that: using System; using System.Collections.Generic; using System.Linq; using System.Text; using System.Threading.Tasks; namespace SoundEditor_V3 { public class Complex { public double real; public double im; public Complex() { real = 0; im = 0; } } } The problem is that it doesn't recognize the constructor as a constructor, now I'm just learning C#, but I looked it up online and this is how it's supposed to look apparently. It recognizes my constructor as a method. Why is that? Am I creating the class wrong? It's doing the same thing for my Fourier class as well. So each time I try to create a Fourier object and then use it's method...there is no such thing. example, I do this: Fourier fou = new Fourier(); fou.DFT(s, N, amp, 0); and it tells me fou is a 'field' but is used like a 'type' why is it saying that? Here is the code for my Fourier class as well: using System; using System.Collections.Generic; using System.Linq; using System.Text; using System.Threading.Tasks; namespace SoundEditor_V3 { public class Fourier { //FOURIER //N = number of samples //s is the array of samples(data) //amp is the array where the complex result will be written to //start is the where in the array to start public void DFT(byte[] s, int N, ref Complex[] amp, int start) { Complex tem = new Complex(); int f; int t; for (f = 0; f < N; f++) { tem.real = 0; tem.im = 0; for (t = 0; t < N; t++) { tem.real += s[t + start] * Math.Cos(2 * Math.PI * t * f / N); tem.im -= s[t + start] * Math.Sin(2 * Math.PI * t * f / N); } amp[f].real = tem.real; amp[f].im = tem.im; } } //INVERSE FOURIER public void IDFT(Complex[] A, ref int[] s) { int N = A.Length; int t, f; double result; for (t = 0; t < N; t++) { result = 0; for (f = 0; f < N; f++) { result += A[f].real * Math.Cos(2 * Math.PI * t * f / N) - A[f].im * Math.Sin(2 * Math.PI * t * f / N); } s[t] = (int)Math.Round(result); } } } } I'm very much stuck at the moment, any and all help would be appreciated. Thank you.

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  • iPhone: CPU power to do DSP/Fourier transform/frequency domain?

    - by mahboudz
    I want to analyze MIC audio on an ongoing basis (not just a snipper or prerecorded sample), and display frequency graph and filter out certain aspects of the audio. Is the iPhone powerful enough for that? I suspect the answer is a yes, given the Google and iPhone voice recognition, Shazaam and other music recognition apps, and guitar tuner apps out there. However, I don't know what limitations I'll have to deal with. Anyone play around with this area?

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  • Can somebody link me to some code where there is a fantastic or a nice use of Inheritance

    - by Soham
    I strongly believe that, reading code and reading good code is key to great programming. If not one of the many. I had been facing some problems in visualizing and having a "feel" of using inheritance to better my code architecture. Can somebody give me some link to good code to emulate, where folks have used inheritance in an absolute "kung-fooey ruthless" manner [in a good way]

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  • C socket programming: connect() hangs

    - by Fantastic Fourier
    Hey all, I'm about to rip my hair out. I have this client that tries to connect to a server, everything seems to be fine, using gethostbyname(), socket(), bind(), but when trying toconnect()` it just hangs there and the server doesn't see anything from the client. I know that the server works because another client (also in C) can connect just fine. What causes the server to not see this incoming connection? I'm at the end of my wits here. The two different clients are pretty similar too so I'm even more lost. if (argc == 2) { host = argv[1]; // server address } else { printf("plz read the manual\n"); exit(1); } hserver = gethostbyname(host); if (hserver) { printf("host found: %p\n", hserver); printf("host found: %s\n", hserver->h_name ); } else { printf("host not found\n"); exit(1); } bzero((char * ) &server_address, sizeof(server_address)); // copy zeroes into string server_address.sin_family = AF_INET; server_address.sin_addr.s_addr = htonl(hserver->h_addr); server_address.sin_port = htons(SERVER_PORT); bzero((char * ) &client_address, sizeof(client_address)); // copy zeroes into string client_address.sin_family = AF_INET; client_address.sin_addr.s_addr = htonl(INADDR_ANY); client_address.sin_port = htons(SERVER_PORT); sockfd = socket(AF_INET, SOCK_STREAM, 0); if (sockfd < 0) exit(1); else { printf("socket is opened: %i \n", sockfd); info.sock_fd = sockfd; rv = fcntl(sockfd, F_SETFL, O_NONBLOCK); // socket set to NONBLOCK if(rv < 0) printf("nonblock failed: %i %s\n", errno, strerror(errno)); else printf("socket is set nonblock\n"); } timeout.tv_sec = 0; // seconds timeout.tv_usec = 500000; // micro seconds ( 0.5 seconds) setsockopt(sockfd, SOL_SOCKET, SO_RCVTIMEO, &timeout, sizeof(struct timeval)); rv = bind(sockfd, (struct sockaddr *) &client_address, sizeof(client_address)); if (rv < 0) { printf("MAIN: ERROR bind() %i: %s\n", errno, strerror(errno)); exit(1); } else printf("socket is bound\n"); rv = connect(sockfd, (struct sockaddr *) &server_address, sizeof(server_address)); printf("rv = %i\n", rv); if (rv < 0) { printf("MAIN: ERROR connect() %i: %s\n", errno, strerror(errno)); exit(1); } else printf("connected\n"); Any thoughts or insights are deeply greatly humongously appreciated. -Fourier

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