13” MacBook Pro Retina (Early 2015) freezes all functions and screen then makes ticking noise

My MacBook has now frozen twice in the span of one week. The screen freezes on whatever I was working on, the trackpad freezes, and the keys do not input anything.

I’ve removed the back cover and gently dusted the inside using pressurized air, but the problem persists.

Here is an audio recording of the ticking noise in question: https://youtu.be/pQhUsNsF9dk

Realistic channel noise patterns

I’m setting up a playful coding challenge for our programmers. It’s about coding theory. There are source, channel and sink. The task will be to implement an encoder for the source and a decoder for the sink. The encoded information will be transmitted over a channel with simulated garbling.

Now my question:

What common noise patterns do we encounter in real settings and how would their transformations look like?

What people talk about are for example:

  • band tapes (stressed at start and end)
  • defect memory (some bits are always off)
  • lightning strike
  • meteor shower

But they never mention how these patterns would look like.

Mac mini 2018 hdmi white noise

I have 2018 mac mini, just bought it a few days ago, running OS X Mojave. And I have 2 monitors connected to it, 1 through usb-c and 1 through HDMI.

Whenever I wake my computer up from sleep, the monitor connected through usb-c wakes up instantly, but the other one takes a while to wake up. Like I could already be using the left screen and the right one is still black.

I tried plugging a different display to this HDMI port and it’s the same story, the monitor takes like 1 minute to start displaying the picture, after I wake my computer from sleep, but, this other monitor instead of just being a black screen displays white noise and then wakes up.

What could be the problem?

In Perlin noise, why need vectors and how to use them exactly?

I’m reading this tutorial on Perlin noise: http://www.angelcode.com/dev/perlin/perlin.html which seems to be the clearest one but still not perfect. A lot of details are skipped and a lot of code unexplained.

My general question is, why do we need vectors for Perlin noise (instead of just noise values at specific coordinates), why do they have to be unit vectors and how do we combine them with the given input point coordinates?

Also, the article gives this piece of code as vector calculation which looks like trying to find out square cell coordinates (except 1 is subtracted instead of being added for some reason):

// Computing vectors from the four points to the input point float tx0 = x - floorf(x); float tx1 = tx0 - 1;  float ty0 = y - floorf(y); float ty1 = ty0 - 1; 

This doesn’t look like any vector operation.

Call makers hear tapping noise whilst i hear nothing, same noise when I record on phone

I have Oukitel K5000, Android 7 android phone.

It had been working and at some point months ago, callers started hearing taping noise.

I just restored it to factory settings but still when I go to recorder… when I record something and hear it back… I hear consistent tapping noise.

What else can I do to fix this ?

Showing that additive Gaussian noise never increases sparsity

Let $ \mathbf{1}\in\mathbb{R}^d$ be the $ d$ -dimensional all-ones vector and let $ n\sim\mathcal{N}(0, \sigma^2 I_{d\times d})$ , show that $ $ \frac{\| \mathbf{1} + n \|_1}{\|\mathbf{1} + n \|_2} \ge c \sqrt{d} $ $ with high probability in $ d$ , for constant $ c$ (independent of $ \sigma, d$ ).

That is, prove that adding Gaussian noise never significantly improves sparsity in the sense of $ \ell_1 / \ell_2$ ratio. Generalization to arbitrary dense vectors are of course welcome.

The mean value of phase noise as a stochastic process

  1. What is the mean value of phase noise as a stochastic process?
  2. Where can I get a theoretical analysis of this topic?

PS: PLL produces cos(2*πfct+φ(t)). The phase noise refers to φ(t). The mean value of the phase noise what I say refers to the mathematic expectation of phase noise stochastic process φ(t), namely E{ φ(t) }.

How to Formalize Noise Protocol Messages

I try to understand the messages of the Noise Protocol Framework. The handshakes are based on Diffie-Hellmann key exchange. This is an example for a handshake pattern:

-> e <- e, ee, s, es 

I try to formalize the handshake with mathematical descriptions. For example the mathematical description of the first message is: . Where is a generator and is the private ephemeral key of the sender. But I don’t understand how the values ee and es are calculated. How can these patterns be descriebed mathematically?

A noise on external screen with MacBook

I have MacBook Pro 13″ 2018. This is Intel Core i5 (2.3 GHz) with Intel Iris Plus Graphics 655 (1536 Mb). MacOs Mojave 10.14.4 is installed. Embedded screen resolution is 2560×1600. I have an Apple’s adapter Thunderbolt 3 -> HDMI as well. What I am doing is connecting Mac to an external display: Dell S2419NH (1920×1080) with HDMI. Mac detects it correctly.

The problem is if I have a close look at the external monitor I see some kind of noise. Like if the white noise was blended with the original picture. This is no problem with colors and if I watch the monitor as usually I do not see any defects. However, after a working day, my eyes are exhausted.

What I have tried:

  1. Replaced HDMI cable with the one I use for my 4K TV at home. No result.
  2. Replaced the monitor with a different FHD. No result.
  3. Replaced adapter with Xiaomi’s one. No result.
  4. Tried a home 4K TV (around 45″). Well, I can not claim that the noise is present. I think my eyes may lie at this point.
  5. Closely looked at Mac’s display. No noise at all, I suppose. If you ask me where is the noise on Mac, on 4K TV or both, I would say it is likely 4K TV that has it. Nevertheless, my eyes are OK after a day on Mac’s display.
  6. Called Apple’s support. The told that monitor is probably Retina incompatible. So, shouldn’t I use FHD displays?

What can cause such an issue? How to get rid of it?