sudo apt install libgdal-dev
git clone https://github.com/PDAL/PDAL.git
cd PDAL
mkdir build
cd build
cmake ..
make -j
sudo make install

List cmake options with installing this GUI

sudo apt-get install cmake-curses-gui ccmake ..
ccmake ..

To get geowave plugin support, you need JDK. Geowave increases the project's complexity so I'll defer that to later, if I get to it.

sudo apt install openjdk-8-jdk

I have a Synology DS918+ NAS running BTRFS. This is 2019 and development of BTRFS began in 2007. So it should be all good, right?

There are interesting features I wanted to try out. Mainly, keeping snapshots. I've also been using EXT3/EXT4, NTFS, HFS, APFS, FAT32, even reiserFS back in 2006-2010, so I've seen my share of file systems. I'm willing to try something new.

I've heard 2 of my friends complain about this file system, but decided to try it because Synology's Setup UI made me inclined to think this is preferable.

I don't know who the target audience for this web UI is, but recommending the BTRFS is a huge mistake. As a home user with a lot of pictures, it is just so unacceptably slow.

How slow is it?

I don't have exact numbers, because they often time out. And I don't want to go through the pain of waiting. I have about 16k jpeg files that in a flat folder. Simple operations are terribly slow.

• Moving 100 files into a folder takes couple of minutes, because there is a copy made for each file moved.
• Doing ls (list all files in folder) takes forever, my terminal hangs up and never returns even after hours.
  • On the Synology Drive UI, it takes minutes, because the web UI is paginated, about 500 files listed per page.
• Deleting files takes as long as moving them.
• Defragging like 1995 is back, so you must remember to periodically "scrub" your disks, which take days (I have two 4TB drives).

I regret so much using this shitty BTRFS system. Unbelievable. RedHat abandoned using this for all the right reasons, it's hopeless. I'll be spending a weekend moving my data to USB disks and reformatting to EXT4.

Never again, BTRFS!

Wrapping up a project at and moving the project to phase 2. We celebrated with a team dinner at Cascal. Actually the first time our small project had outside dinner.

I didn't take notes at the time so these are the memories from last night.

I will roughly summarize the events and some principles. I won't write much into details partly for sake of privacy and partly because I can't remember nor have the time to flush it all out. So I'll write some memory triggers.

I had a bunch of questions in revolving around:
- What is going on with the project?
- What are the plans for our other projects?
- What were the lessons learned from building a company?

And other personal and life questions as
- Why did you create the company?
- How come you don't want to be a professor anymore?
- Why did you want to become a professor?
- How did you climb the life ladder from where you started?
- What are your principles?
- How did you get to where you are today?

And some summaries of his responses
- work very very hard
- be opportunistic, they come and don't wait
- surround yourself with people you want to become

I'm in the middle of migrating the this website from hosted on a shared namecheap server to a VPS server. Namecheap notified me that this site will be undergoing for hardware upgrade, moving to a different physical server. Let me remember what the old hardware was, and I'll update this post later with the new hardware. Schedule update is for Mar. 25, 2017.

[phepha@server159 ~]$ cat /proc/cpuinfo
processor : 0
vendor_id : GenuineIntel
cpu family : 6
model : 62
model name : Intel(R) Xeon(R) CPU E5-2620 v2 @ 2.10GHz
stepping : 4
microcode : 1064
cpu MHz : 2099.884
cache size : 15360 KB
physical id : 0
siblings : 12
core id : 0
cpu cores : 6
apicid : 0
initial apicid : 0
fpu : yes
fpu_exception : yes
cpuid level : 13
wp : yes
...

[phepha@server159 ~]$ cat /proc/meminfo
MemTotal: 65786112 kB
MemFree: 740052 kB
Buffers: 10627716 kB
Cached: 38361744 kB
SwapCached: 0 kB
MemCommitted: 1031798784 kB
VirtualSwap: 0 kB
Active: 29757552 kB
Inactive: 25222428 kB
Active(anon): 4747632 kB
Inactive(anon): 1283136 kB
Active(file): 25009920 kB
Inactive(file): 23939292 kB
Unevictable: 5244 kB
...

[phepha@server159 ~]$ cat /proc/version
Linux version 2.6.32-604.30.3.lve1.3.63.el6.x86_64 (mockbuild@build.cloudlinux.com) (gcc version 4.4.7 20120313 (Red Hat 4.4.7-16) (GCC) ) #1 SMP Sun Sep 27 06:34:10 EDT 2015

```
sudo apt-get update
sudo apt-get install -y software-properties-common
sudo add-apt-repository ppa:certbot/certbot
sudo apt-get update
sudo apt-get install -y python-certbot-nginx
```
Then get a certificate `sudo certbot --nginx certonly`

Now edit the nginx config to add the fullchain.pem and privkey.pem paths.

sudo vim /etc/nginx/sites-enabled/default

Add lines 22-25.

server {
20 # SSL configuration
21 #
22 listen 443 ssl default_server;
23 listen [::]:443 ssl default_server;
24 ssl_certificate_key /etc/letsencrypt/live/autox.network/privkey.pem;
25 ssl_certificate /etc/letsencrypt/live/autox.network/fullchain.pem;
...
}

"In mathematics, the four colour theorem, or the four colour map theorem, states that, given any separation of a plane into contiguous regions, producing a figure called a map, no more than four colour are required to colour the regions of the map so that no two adjacent regions have the same colour." -- Wikipedia

"A codebase composed of small interconnected modules that communicate with each other in a well-defined way will have a lower complexity than one where the modules communicate with each other haphazardly." - http://alexkudlick.com/blog/what-the-four-color-theorem-can-teach-us-about-writing-software/

One time I accidentally messed with the `/etc/passwd` and locked myself out of being able to SSH into the machine. Since this is a remote machine in AWS I had no way of doing what I'd normally do. Which is attaching a keyboard and monitor and fixing this manually.

To fix, use the AWS EC2 Management page to:
- spin up a new instance of vanilla ubuntu EC2 (let's call it David)
- shutdown the locked machine (let's call it Goliath)
- unmount Goliath's volume
- attach the volume to David

Then follow this guide: https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ebs-using-volumes.html

Summary of what I did from this guide:
```
lsblk
sudo file -s /dev/xvdf # MBR (not data type)
sudo file -s /dev/xvdf1 # ext4
sudo mkdir mount_folder
sudo mount /dev/xvdf1 mount_folder # ext4 mounted
cd mount_folder
# undo crazy setting (see Note #1)
cd .. # to unmount
sudo umount /dev/xvdf1
# Note #2
```

Note #1: For me I tried to modify `/etc/ssh/sshd_config` to allow one more user to login. But this made me unable to login after. So I removed the offending line.

Note #2: now in the Volume webpage
- undo attach to David (Volumes tab)
- mount to Goliath (Volumes tab: attach as EBS path /dev/sda1)
- boot up Goliath (Instance tab)

Done/No,Description,Amount
TRUE,Cooler Master MasterLiquid Lite 240mm Liquid Cooling,$42.89
TRUE,Intel i7 7700 CPU,$269.36
TRUE,ASRock Z270 KILLER SLI/AC Z270 Motherboard,$128.69
FALSE,Mobo MIR,-$30.00
TRUE,"EVGA SuperNOVA 1000 G2, 80+ GOLD 1000W, Fully Modular",$118.51
TRUE,Thermaltake Core G21 Dual 4mm Tempered Two-Toned Glass Power Cover ATX Black Gaming Computer Case ,$64.34
FALSE,Case MIR,-$20.00
TRUE,Crucial Technology 32GB (2x 16GB),$259.99
TRUE,ZOTAC GeForce GTX 1060 6GB Mini,$267.89
TRUE,Toshiba Solid State Drive PCIe NVMe M.2 512GB,$247.90
TRUE,Windows 10 Home,$92.99
TRUE,Oculus Rift Headset,$679.99
TRUE,Oculus Touch Controller,$99.99
FALSE,,
,Total,"$2,272.54"

SLAM is simultaneous localization and mapping; the goal is to build/update a map while simultaneously keeping track of location within.

In other words, SLAM takes sensory data as input (such as camera, lidar, ultrasound) and outputs a partial map and location within.

A popular open source framework is called ORB SLAM. This is fast, robust, and efficient.

I'll summarize my findings here.

• https://github.com/raulmur/ORB_SLAM2
• https://github.com/dorian3d/DBoW2

There are three papers you can read that describes how it works. You should read them in order, because the ideas build on each other (1-2) and (3) describe improvements made.

1. Bag of Binary Words
2. ORB-SLAM paper
3. ORB-SLAM2 paper

I'm not much of an expert in this field, so I'll try to explain in layman's terms of how I understand it. Here are essence of what you need to know.

• Sensory data are a stream, but it can be discretized, such as a video stream.
  • Discrete means to represent a real space using a discrete quantities.
• Videos are sequences of images.
• In an image, there are points that can be easily identified and tracked across images.
• These points can be identified by an algorithm called Bag of Words (read 1).
  • A picture is like a paragraph, it can be described by visual words.
  • Visual words are small images that represent like noses, eyes, mouth.
  • With enough words, you can guess what objects you're looking at, and from what angle.
  • For example, if you had two eyes and a nose, you would guess a face.
  • And if the two eyes are much bigger than the nose, you're likely looking downward angle to the face.
• Bag of Binary Words improve performances of the Bag of Words (BoW) model.
• The ORB-SLAM system uses the BoW model to build a database.
  • Database stores BoW features that represent an image.
    • Meaning the image was converted into BoW features.
  • These features are used as an index into a location map.
• From an image and a location, a Key Frame can be created.
  • Key Frames are an association container, which can represent a mapping of BoW features into a frame in location space, and vice versa.
• Using these BoW features, and sensor calibration data, a location is triangulated from mathematical calculation.
• Many of these Key Frames are linked together.
  • You can think of Key Frames being a vertex in a graph, and an edge represent co-visibility.
    • That is, the BoW feature is visible from the other Key Frame as well.
  • This graph is called a covisibility graph.
  • Which represents a map.
• Having this graph aids in knowing when to insert a new Key Frame into the database.
  • For example, when two Key Frames have less than 75% common BoW features.
• When given an image, the database is asked to return a list of plausible locations.
  • The image is converted into BoW feature.
  • This feature is used to search the covisibility graph to find similar Key Frames.
  • Returns a list of Key Frames with statistical likelihood to represent which Key Frames contains the most matched BoW features.
  • The covisibility graph reduces search complexity, therefore speeding up search, and also reduces storage needs.
• So now a map is being built at the same time it is localized.
  • SLAM.
• In the future, we can come back with more data to:
  • Improve the map.
  • Ask the database for a localization result.

That's all folks. I'll add some pictures later.

Here's a video.

Clion (actually you do not need to purchase a license if you use the EAP (Early Access Program) license. Downside is you have to download a new one each month. But it'll be free to use for 1 month, and you don't have to pay $199 USD per year.
https://www.jetbrains.com/clion/nextversion/?fromIDE=

HackerRank
Terminator
Sublime
OpenCV
Python
Tensorflow
Slam mono
Slack
Fish Shell
Git/GitLab
C++11