# The Most Important Thing You May Not Know About Hypothyroidism

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When you take D-3, you also need to supplement with Magnesium. It means that the coordinate is 1 when projected onto the x-axis, and 1 when projected onto the y-axis. I am hypothyroid as well, and I have been following a program by Tom Brimeyer called Hypothyroidism Revolution, look him up. In this case, cycles [0 1 1] generate the time values [2 -1 -1] , which starts at the max 2 and dips low You encrusted your way with selfishness and found time only to guard your cherished soles from the thorns which you yourself grew.

## THE MANGO KID

To delete everything, select All time. Next to "Cookies and other site data" and "Cached images and files," check the boxes. In other browsers If you use Safari, Firefox, or another browser, check its support site for instructions. What happens after you clear this info After you clear cache and cookies: Some settings on sites get deleted. Some sites can seem slower because content, like images, needs to load again. They make your online experience easier by saving browsing data.

We also share new trends, how-to-wear-it ideas, and styling tips that will help you focus on need-now updates, from the latest must-have style of jeans, to the ideal layering piece to wear in-between seasons, or a pair of running shoes made with the latest technology. For even more fashion insights, find us on Facebook Facebook.

Learn more about Amazon Prime. Amazon Fashion Amazon Fashion is a one-stop destination for head-to-toe style. Your recently viewed items and featured recommendations. View or edit your browsing history. There's a problem loading this menu right now. Get fast, free shipping with Amazon Prime. Get to Know Us. Hah, just a curious learner here. Negative values in time, for the signal you mean? I just figured out how the transform works on my own.

Its a little mathematical machine, and it is an extremely intuitive one. Thanks for the explanation. The old Yamaha DX synthesizers series used frequency modulation to create, from 4 to 6 sine waveforms, quite complex sounds. Cool background — it seems to only take a few components before the shapes get really intricate.

Is there anyway to visualize orthogonal signals? Or understand it intuitively instead of just saying that their dot product is zero. I just wanted to learn digital signal processing… After a couple of chapters in my dsp book i noticed that i have to study signals and systems first in order to understand fully dsp. It became clear very soon that i need to learn more math especially fourier analysis to make sense of everything in my signals and systems book.

This could not get better. I have been trying to understand this concept on my own and it has been a long difficult task. But with this post of yours, my life is easy now. Keep up the good work man! Because its a damn circle and X and Y are perpendicular. Imagine two signals driving a car. One controls the speed, the other controls the direction. When the speed is at max, the direction might be null no direction, the brakes.

And when the direction is north, the speed might be null no speed, the brakes. Sometimes they are both on North at 10mph, or South at 10mph. Over all time though, the car does not move, because the sum of all contributions cancels. Great feedback, I really like knowing which parts can be clarified. I was struggling with Fourier for quite some time.

Wikipedia and other web based explanations are way too complicated for my rudimentary knowledge — and thus, useless. Thanks for explaining a difficult concept so elegantly. Would you please be kind enough and consider doing the same magic and explain the concept of Claude Shannon Entropy? Really glad it clicked, thanks for the note! Yes, often times people jump into extremely technical discussions of math without laying an intuitive foundation. What is a circle?

Just a circle itself? Why not considering a circle the son of a cone? Or maybe a circle is just a straight line for bug living in an infinite radius circle. Thank you very much for this work sharing your insights, there has been very practice for my math career, keep going!

I wonder why most books about periodic phenomena most of the time instead of using circles, use a trigonometric description or even more , a complex exponential description? So in other words what is the geometric picture of this two circles multiplied together? I wonder what the circle based description would be for a two dimensional Fourier transform?

If we have sinusoidal AC voltages and currents, how do we multiply the corresponding two circles for finding the instantaneus AC power? What is the geometric picture?

Not sure why most books jump to the most technical definition first: Intuitively, I imagine a circular path, and on that circular path, another circle is traveling [a bit like how the Earth moves around the sun, and the moon moves around the Earth]. The combined effect of the two positions is the net power seen. What is the practical use of this circle view approach in solving practical problems?

This is the best ever intuitive presentation of Fourier! And the animations…gr8 work…Thanks for the effort…Similar insights on Wavelets might be of gr8 help too…pls consider it…. Thanks again…keep it going!!! This is a work of a an extremely talented and gifted person!!! Because characteristic function of a probability density is Fourier Transform, so it needs to be time and cycle driven, but I just not sure what does cyclicality have to do with probability..

Thanks for the note! Wavelets would be a good follow-up. I need to learn more about them. Really appreciate the kind words. All I have to say is that you have put together a wonderful article. Your style of writing immensely helps in removing the apprehension in the mind of the reader of having to deal with a complex topic. I plan to write articles in this domain the help students and professionals maneuver complex topics in these subjects by presenting them in a easy to grasp manner.

My article on Fourier Transforms http: Would be very thankful if you can provide your feedback. This explanation of the Fourier Transform is an excellent example of it. Please write some textbooks. Wonderful and revelatory stuff — you make learning it a delightful experience with all the visual metaphors and animations gradually building to the abstract formulas.

The formulas themselves just confused me. Now i really feel I have a handle on it. Thank you for the effort in making this. Hope everybody who gets swamped in this domain comes here.

I like your site. Yep, part of writing is getting in the head of the reader and gently going down the path, vs. I love hearing about other people doing their explanations.

Everyone has a different style, so looking forward to checking out yours. Hoping to do some more material on Calculus, Trig, etc. It takes gr8 effort and flair to be consistent and maintain this network of better explained!! I have a question to this article…the first cosine wave we simulate from a circular motion and the sine wave in the referral link provided, vary in explaining the amplitude….

The analogy is rather out-of-place, confusing and seems like an unnecessary detour. Most important of all, the author seems to lack primary insight on the subject himself. Smoothie is a whole and its ingredients are parts. It is not the same with signals at all. They are all whole in their own domain. The analogy is totally misplaced and the information provided only partly correct.

The key idea of FT — change of variable — is not emphasized at all. Only well informed people should be allowed to author such articles. That said, there are a couple of good insights for new learners — 1. What a shame that some feel the need to squelch the brilliance of others, presumably to bolster their own inadequacies. To this end, I am confounded by your own statement that the smoothie is not an apt analogy.

But that does not preclude the ingredients themselves from being whole on their own. Before berries are thrown in the blender, they are just that: Regardless, this distinction is primarily one of taste— the important observation is that a signal can be represented as a sum of Fourier modes in the same way that smoothies can be represented as a union of ingredients.

The features of this analogy carry through quite naturally, and the aspects that do not are clearly addressed by the author. I feel we might hear more of him in the future. I hope you will hear more of me in the future.

I will keep this article as an example of how intellectual work should be done. Hi Simon, thanks for the note — hope your nephew enjoys it: Really appreciate the kind words, I hope the strategy of finding specific examples to illuminate abstract concepts gets more traction. The time values [1 -1] shows the amplitude at these equally-spaced intervals. Like for a 1 Hz signal why are you measuring at 2 points, for a 2 Hz signal at 3 points, for a 3 Hz signal at 4 points and so on? Does this have something to do with the Nyquist-Shannon sampling theorem?

Hi Niko, great question. Each animation is over the course of 1 second. If you are analyzing a 1Hz signal inside that interval, you just need a measurement at the beginning and halfway at 0. If you only had the measurement at the beginning 0. If you are trying to measure a 2Hz cycle which goes up and down twice during the period , then you need at least 2 measurements beyond the starting one so at 0.

Understanding Fourier Transform cika. Kalid, could you possibly tell us what software you use to create these animations?

How do you accomplish these? You can open http: The details of how to do web programming will probably need a few more articles though! I was looking through your material on fourier transform and its by far the best explanation I have found anywhere. I spent a few days reading this and I understand everything except for one hiccup.

Can you please lead me on the right track? There are several versions of the Fourier Transform, they key is realizing you need to average the strengths somewhere along the way when you apply the forward transform and then the reverse. If f x is the forward transform and F x is the reverse, then you can have:. Either way, after doing f F x or F f x , i.

If you have a single instant a spike in time like 1 0 0 0 0 0 … , then its magnitude should be shared among every possible frequency which can claim it?

Hey Kalid great work on this one. I started learning about EEG signal analysis and the Fourier tranformation comes up constantly. I never heard of it before and for such a beginner this is a great, very helpful, article. However to better understand everything about what you said I have a couple questions which I hope you can answer:. In reality, is the signal not comprised of waves of varying amplitude? Think of it like this: I repeat, this article is basically flawed because the analogy does not capture the true essence of the Fourier transform.

No doubt, the animations are nice and the article is well-written, but I have problem with the content, and anyone who understands Fourier transform well would have the same problem. It is unfortunate that some people are unable to accept healthy criticism without descending to provocative language.

Well explanation is very good but i m stuck at some points. A strength of 0 means that cycle ingredient is not present in the signal. I am a retired mathematics teacher and I have to say that one of the most inspired pieces of teaching that I have seen. I have a basic maths understanding but am not a mathematician, and have found most descriptions of Fourier transform to be utterly impenetrable. However this article presented exactly what I needed, for my purposes, and the interactive animations helped greatly too.

Probably in a real application the overall time interval would not be 1 second, and therefore the frequencies would change accordingly. If the time interval were 2 seconds then you would actually have 0Hz, 0. I think there is a mistake where you introduce the formulas in the end. The formula for timepoint and frequency are swapped. Anyhow, thank you so much for this!

I was just trying to produce the same results as in the article. Hi Thomas, no problem! I should probably clarify that point. The section where you introduce the animations needs to be clarified. The way it is written confuses me.

When you change one it automatically changes the other, why? Why is there always a 0? Not good at all. I was confused and you added spices into it. The animation controller are the worst. Isolating the individual frequencies is tricky. Let me expand on the analogy in the post. Imagine you have a bunch of toy cars, racing around a circular track. We could have an East-West position and North-South position over time. If this treadmill is going 10mph, then cars going exactly that speed will stay still.

The other cars are going either faster or slower, and will continue to circle around the track over time, their average contribution will be nothing. Only cars matching the speed of 10mph will stick around, and can be measured. Maybe we see 3 cars going that speed. The Fourier Transform takes the notion that any signal really has a bunch of spinning circular paths inside.

That is just the treadmill: Btw, appreciate the support. Basically, we have two ways to describe a signal: The cycles have various strengths how much of each ingredient to use. When you change either side, the widget converts the new values to the other.

So, if you add a different set of time points from 1 1 1 1 to 2 2 2 2, for example then the corresponding cycle ingredients are adjusted. If you change the cycle ingredients, the time points they lead to are similarly adjusted. Or do something equivalent, but probably a bit more efficient maybe.

There are little hairs cilia in you ears which vibrate at specific and different frequencies. Because of this, you can distinguish sounds of various pitches! So, our ear is setup in a way that each hair is tuned to react at different frequencies. Nature usually has ways to do everything in parallel, while our computers manually crunch through. Quick notes sticky post synthtech. I dint know what Fourier transform is, one hour ago so this may be stupid question.. Fourier transform has all positive values then how can it give back a signal with negative values??

Similar question shateesh had asked about but ur answer dint satisfy me: Can you add at least one graph of sample Fourier transform for people like me: Hi Great article, especially for somebody like me with no previous Fourier experience. I have one question that is still confusing for me and it would be great if you could help: I am expecting this value to be 1 and not 2. What am I missing? The Fourier Transform is based on circular paths, which start at an angle of 0 [neutral], go positive [90 degrees], back to zero [ degrees], negative [ degrees], and back to neutral [].

By aligning and delaying various circular paths, you can reach the negative numbers. In general, you can modify a positive signal by starting each cycle at the opposite side to make it negative.

In the calculator linked, try entering [1 0 0 0 ]. The result is [1 1 1 1], which appears to have magnitude 4, even though the input signal had magnitude 1.

I am a junior in DFT, actually i just heard of fourier transformation for the first time shame on me, i know , and tried the wikipedia explanation. I cant say i understand everything just yet, ill need to work a lot harder for that.

But i did have a very clear theoretical and practical idea of what im about to study now. Dnx a million, you make science sound like less science. Nice job with the graphs, and good idea the challenge to try 0, 0, 4, 0. That was the point when i finally really understood. Awesome, glad it clicked for you. Thanks for letting me know the examples helped. Thanks, you are correct. I was very loose with my terminology, to avoid the need for decimals.

If a signal had 4 data points a b c d , I wanted to imagine scaling it up so it took 4 seconds of time to complete. Similarly, something which completed half the cycle each step. This is a mental conversion I was running in my head, and I need to clarify this part, thanks!

New mathematical techniques might allow for X-ray nanocrystallography National Academy of Sciences. Quick notes , September to November synthtech.

Suddenly I discovered the meaning of your site to make money isnt it? My goal is to help people grok the ideas I struggled with. Next time we would take permission from you whether to write blog or not.

I just made a 2D fft filtering tool on my website, you can mask off regions of the spectrum as a filter and see the effects by performing an iFFT on the spectrum. And i must say you did the best.

Thank you again to explain it so clearly. Trying to understand your animation — the first one http: And I assume on the l. But how do you get the circle. If you just have a cosine wave, you will oscillate along the x-axis. Sine and cosine can be defined on the unit circle, see http: A very good explanation, but I wonder if it might be a bit too oversimplified in places? Simplification is good but i think calling them position 0 1 2 3 and then recycling arabic numerals for many other purposes just gets confusing after a while.

Just a smaalll request…. Hi Shweta, glad you liked it. Why not at any other points? Hi Ravi, good question. We need to make measurements quickly enough to capture the fastest-moving signal. Start off with a 0Hz signal, which is constant. How many measurements do you need? Now, how about a cycle which repeats once during the signal?

First, we want our samples to be evenly spaced, but also, we need two measurements to describe the behavior of that 1Hz cycle. A 2Hz cycle is similar: I will be spending more time here. I appreciate both your use of simple analogies and your disclaimer as to their limitations; I like your raft and river comparison for how far an analogy can take you.

I recently came across a curious discussion of how Fourier transforms may be used in quantum physics to explain how a particle like an electron can appear to be both particle and wave together.

I had some trouble visualizing some of the descriptions but this site with the interactive animations pulled it all together quite well for me. To those who doubt the usefulness and need for analogy, I leave you with the words of Erwin Schrodinger: I recently bumped into the site and read few articles.

Each one gave me Aha moment. Thank you for that. This is great explanation for those who struggle with math and want to click without being a math guru. The part I struggle the most with is seeing a sound as a whole in the time domain, sampling it and applying the ft.

How does this word looks in the time domain graph? Would we have to divide whole 1second into every hz value and spread out accordingly? If above is true the next step would be to sample the time domain? What does the sample contain? What is the sensor device that can sense all those different frequency to allow sampling, ft and so on. I understand that ft converts all signals into one but above drives my brain crazy. Just to add up to my question.

Is the signal coming to microphone the magic devices I though about in my question already a sum of all signals within the frequency range of the microphone? In other words is the sound coming to mic the total of all frequencies that could be constructed via FT if we would know every frequency value? Firstly, congratulations on a well written article. I love the idea behind the site. If I understand you correctly any signal is a series of time spikes, whose frequencies are phase shifted to produce constructive interference at each spike.

However, I tried computing the signal 4,4,0,0 using the method you describe in the full analysis section and my 1hz term gave me an amplitude of 2 and not 1. Pradeep Maskeri Good observation and question. Let me see of I can fill in some of the details. The Fourier Transform FT is not exactly a rotational transform in the way you are thinking. Instead the FT takes one thing that can have an associated idea of rotation, and transforms it to another thing that can also have an associated idea of rotation.

The FT does not take one complex number and transform it to give one complex number that has been rotated, instead it takes one method of representing a complicated bunch of stuff and transforms it to give a different description of the same stuff. The forward and reverse transform then are not forward and reverse rotations of a complex number, but rather the following: Bart Your second question points toward the answer to some of the missteps in the first question.

In the physical sciences it is a beautiful thing to make missteps and wrong guesses, greater still to voice them and ask questions. I can answer your question by telling you a thing that I claim is true.

Imagine the chagrin of the poor souls who came up with the corpuscular theory of light http: We know this because the equations that govern it are, well, wave equations… but I digress. You are right, the sound arriving at the microphone is already the sum of all frequencies.

What arrives at the microphone is just a single pressure wave with a complicated shape, not a whole bunch of sin and cos waves. The Fourier Transform FT just lets us look at it as if it were a bunch of sin and cos waves.

Then we can perform some tricky math with it. We can send it through a computer and do all sorts of jazzy things like take an old Parlophone mono recording of a concert with a rude telephone ringing in the background and remove just that sound.

I saw a recording engineer at Abbey Roads studio do this with some pretty fancy software, blew my mind! For a lovely game of chasing rabbits down holes, go ahead and see what Wikipedia has to say about the Nyquist Sampling Theorem. Let me see if I can shorten it. We want to record it to play back later, but we want to record it digitally!

Start with what the mic gives us, an electric voltage wave that has the same complicated shape as the pressure wave that hit the mic.

The strength will just get converted to a number this is a binary number that can be stored digitally. By the Nyquist Theorem, twice for each cycle of the highest frequency we want to represent.

Humans have a tough time hearing any sound at a frequency higher than 20K Hz repeats every 0. So you take 2 samples every 0. Each sample is an 8 bit number. Store it as a file. I left out a detail or two. This lets you faithfully record a sound up to 20kHz. If a higher frequency sound hit the mic our sampling would have missed it. It can be about anything that is represented as a wave like a quantum wave function for an electron, or an atom, or a cat, or the universe.

If the wave is the quantum waveform of a photon we may like to view it the same way. Yes it was math alone that caused him to propose his Uncertainty Principle. The Fourier Transform just gives us a way to go back and forth between the parts, and the composite. Its up to us to decide which version we want to work with.

When combining the spikes for 4, 0, 0, 0 and 0, 4, 0, 0 we need to take the phase shifts into account. The terms that are perfectly in phase aligned can just be added, so the 0Hz component becomes But how about the 1Hz component? In the first spike it has phase of 0, in the second spike it has a phase of This is like going 1 mile East 0 degrees then 1 mile South The other terms can be worked out similarly.

They can even cancel when fully out of phase 1: My ah ha moment with Fourier is when I looked at the trig identities and realised that two sinusoids multiplied together resulted in another sinusoid centered on the x axis, unless they were the same frequency, in which case the result would be all above the x-axis, basic orthogonality. Hello everyone,i am doing my project in image processing..

I have found that moving part pixel intensity values becomes dominant means its intensity values are increased so much compared to stationary part intensities in reconstructed frames of original frames.

This abstract perspective helps you practically too. You become less reliant on formulas since you have a good global understanding of the notion of a Fourier transform. The goal was to filter a signal into parts for easy analysis, which can be done via an integral, or perhaps mechanically our ear essentially runs a mechanical Fourier Transform on the incoming sound waves, and as a result we can hear several sounds simultaneously , and so on.

The latter is the most specific, but not likely to be the most approachable or helpful to a newcomer. From there, I can appreciate both the abstractions and subsequent implementation details. When writing, I basically write for myself — what do I wish I had heard up front? I found this article incredibly helpful as a high school student in need of college-level mathematics concepts. One thing about it, though: First thing is that as soon as you move the FFT into 2-dimensional space it moves very quickly away from the core ideas of this thread.

Secondly, I think that it is not clear exactly what you are asking. As far as I can tell, what you are doing is 1 taking a time series of images and running a 2D FFT on each.

Then 2 throwing away the amplitude data and 3 Inverse transforming the images back into the spatial domain. The real problem is in the statement of the next part of the problem — to wit: I suspect that you are referring to the reverse transformed images. Anyway, keep in mind that the FFT tells you about periodicity. In 2 dimensions, this means how bits of the image are spaced and oriented.

In this section on Fourier transforms: After half a second we should be at the same spot: I might be missing something basic, or there is a mistake. If it is 1 circle per second, then I would say after 1 second we would have completed a circle and be back to the same place or starting point. How, on a circle, would you be back at the same spot after degrees?

This link might be helpful for those who want to understand it from the frequency perspective: I have noticed a small typo in the Appendix: Projecting Onto Cycles section. You have mixed up the Fourier transform with the inverse Fourier transform ;. So how would you create a linear trajectory with a sum of sine and cosine graphs, any amplitude?

Sorry it took so long to reply but here is how I would answer your question. You need to use the full integral definition instead. You should get some function of w that is a complex exponential. This will give you the indefinate integral.

Depending on what you want, you will probably need to handle infinities in the integral limits when finding the definate integral — a real pain but doable. Admittedly this is all very tedious but it can be made to work. As a follow-on, you might be interested to know about the old Maxim that a Square Wave is made up of an infinite number of even harmonics while a Triangle wave is made up of an even number of Odd harmonics.

In either case the amplitude of each term decreases as a function of frequency. If you get the phase and amplitudes right, you will see a really good approximation of a square wave or a triangle wave emerge as you add harmonics. Hello everyone,i have a doubt…. I know that for a given signal, the sampling frequency Fs must be twice or more than maximum frequency of the signal Fm.

It is easy to understand the concept for a 1D signal. So can anyone help me regarding it.??? Ugh how is everyone else understanding this? I got so confused after the applet was introduced.. Are you saying that every wave is a sum of infinitely smaller waves? So, take some complex periodic waveform. By definition, it must have a period and it must repeat identically in each of these periods.

This period is the fundamental frequency. Well if we remove the fundamental what are we left with?