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  • Aimee Gonzales 1:08 pm on May 5, 2017 Permalink | Reply

    Fourier Transform for Hearing Devices 


    A hearing aid has approximately 9 – 16 microphones, each receiving different sound waves. Fourier transform comes into picture when filtering out these sound waves. Hearing aids are also designed to adjust well based on the environment. That is, the sound of the person you are talking to still stands out despite despite the different noise sources in the environment (thanks to Fourier transform).


    Source: https://cadcammodelling.wordpress.com/2011/04/14/fourier-transform-and-its-applications/

  • Aimee Gonzales 9:13 pm on February 10, 2017 Permalink | Reply

    Symmetry and General Relativity 

    One may think, “Woaah! Is there a connection between symmetry and general relativity?” The answer is yes, but the relationship is not that obvious.

    Albert Einstein, the main proponent of the Theory of General Relativity, was a visual person. Whenever he encountered a concept in his studies, he tries to imagine the image of that particular concept.

    For example, imagine someone moving towards you holding a light source, say a flashlight, with a speed of 1000 meters/sec. You would think that the photons would be travelling at (300 million + 1000) meters/sec. However, this is wrong. Einstein said that the photons would still be travelling at 300 million meters/sec, in relation to you and the person holding the flashlight.


    Where does symmetry enter? Symmetry is represented in the concept of the “conserved quantity” – something that retains itself no matter the perspective in which you look at it. This is basically symmetry. In the example above, no matter where you are located or where the flashlight holder is running from, the photons will still travel at the same speed, as if some underlying symmetry is present.


    Source: https://www.thenakedscientists.com/articles/features/importance-symmetry

  • Aimee Gonzales 1:44 pm on January 27, 2017 Permalink | Reply

    Data representation 

    Vector spaces are relevant in most Data Science problems where a dataset arranged in rows and columns (rows are data, columns are attributes). From this, we can picture that a dataset is an mxn matrix. To be short you can approximate any point in your data as a linear combination of some vectors, a base of a vector space. The choice of base depends on the problem you are trying to solve, different algorithms create different bases for example algorithms such as SVD/PCA, ICA, NMF and K-Means will create different bases.

    So from the point of view of Data Science a vector space creates a representation of data from the point of view of a given base and thus it’s a very powerful and important concept.

    Source: https://www.quora.com/What-is-an-application-of-vector-space-in-computer-science

  • Aimee Gonzales 12:45 pm on January 18, 2017 Permalink | Reply


    Week 1: Introduce Yourself

    What’s your name?

    Hi! I’m Aimee Nicole Gonzales. Aimee, for short 🙂

    What were your thoughts when you enrolled in this course?
    This is my second 13x subject. I thought that I am finished with math, but 130 is another math subject (disguised as a CS subject). I wonder if I still remember what I’ve learned in the 50 series.

    How comfortable are you with math?
    Ever since my elementary days, my parents really made sure that I appreciate the value of math. They enrolled me in various math programs. I even attended MTAP since Grade 1 until 4th year high school. My journey with math is quite happy, I guess. I do appreciate it a lot when the theories are applied and not just stated for lesson’s sake.

    What’s your dominant feeling right now?
    Honestly, I’m trying to be optimistic about this semester. I feel that I need to put in an extra effort to pass my subjects (which means I need to sacrifice a lot).

    • Paul Rossener 11:39 am on January 22, 2017 Permalink | Reply

      I applaud your parents, Aimee, for supporting you at a very young age. CS is full of math, so appreciating the importance of math can take you a long way.

      Liked by 1 person

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