145 lines
7.2 KiB
Markdown
145 lines
7.2 KiB
Markdown
---
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title: "Dev & Engineering"
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date: 2021-01-17T22:59:40+00:00
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draft: false
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aliases:
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- /dev
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---
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{{% image-box-link src="/posts/visual-search/mapSurfaceWithMax2.png" href="/posts/visual-search" title="Visual Search" caption="MATLAB" %}}
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{{% image-box-link src="/posts/lpss/hood_m_gram.png" href="/posts/lpss" title="Speech Synthesiser" caption="MATLAB" %}}
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{{% image-box-link src="/posts/markov/StateTopology.png" href="/posts/markov" title="Hidden Markov Models" caption="MATLAB" %}}
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# [Infrastructure]({{< relref "infra" >}})
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I manage my local and cloud infrastructure with __Terraform__ + __Ansible__ + __Docker__. I've found this stack incredibly powerful so I've written a post about my patterns and what I love about it.
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{{< figure src="/posts/infra/grafana.png" alt="grafana dashboard" >}}
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Basically, Terraform creates and destroys infrastructure, Ansible manages the OS-level stuff and then the services I run use docker compose. I use a _bootstrap_ Ansible role instead of golden images.
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[Read More]({{< relref "infra" >}})
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---
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# [Holoportation](/holo)
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`C++ [Kinect SDK, OpenCV]`
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`C# [Winforms, Unity 3D]`
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Holoportation is an area of research exploring the ability to livestream 3D environments over the internet. The technology has many applications for __AR/VR__ experiences like 3D sports and music events or smaller-scale applications like a 3D __Twitch__.
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The holograms are captured in the form of a __point cloud__, a cluster of coloured dots that, when presented correctly, can appear like the original object.
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{{< figure src="/images/holo-avatar.jpg" >}}
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My undergraduate dissertation documented extending the [__LiveScan3D__](https://github.com/MarekKowalski/LiveScan3D) holoportation platform to allow multi-source streaming. The existing capabilities allowed a single scene to be captured in real-time and streamed elsewhere for viewing, multi-source allows multiple independent scenes to be received and composited at the same time (a many-to-one system).
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{{< youtube NP0aVjuk5fU >}}
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[Read More](/holo)
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{{% image-box-link src="/posts/draught/checkers-board.png" href="/posts/draught" title="Draught" caption="Rust + Js" %}}
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{{% image-box-link src="/posts/game-of-life/gameoflife1.png" href="/posts/game-of-life" title="Game of Life" caption="Rust + Js" %}}
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# [Mixonomer](/mixonomer)
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`Python [Flask]`
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`JavaScript [React]`
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Mixonomer is a web app for creating smart playlists for __Spotify__. These playlists watch other playlists to use them as sources of tracks. These tracks are filtered and sorted before updating a __Spotify__ playlist.
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Updates are run multiple times a day or on-demand. Additionally, __Last.fm__ integration provides listening statistics for your playlists.
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{{< figure src="/posts/mixonomer/cloud-structure-3.png" alt="cloud structure" >}}
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The project began as an exercise in recreating the functionality of [__Paul Lamere‘s__](https://twitter.com/plamere) [__Smarter Playlists__](http://playlistmachinery.com/) app. This tool had become a really important part of my daily listening habits as a way of combining my smaller sub-genre playlists into larger mixes.
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The app has a __Python__ back-end written in __Flask__. The front-end was built using a __Node + Webpack + React__ stack.
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The system is now deployed with a fully serverless architecture.
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[Read More](/mixonomer)
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[Try It Out](https://mixonomer.sarsoo.xyz/)
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[Source Code](https://github.com/Sarsoo/Mixonomer)
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---
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# [Selector](/selector)
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`.NET [ASP.NET, Redis, Docker]`
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`TypeScript [Vue]`
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A __Spotify__ listening agent which watches what you listen to and presents related data and information in a live dashboard. __Spotify__ presents some interesting track data that isn’t visible in the official clients such as its beats-per-minute, key signature and a musical descriptor.
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{{< figure src="/posts/selector/dashboard.png" alt="dashboard" >}}
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[Read More](/selector)
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[Try It Out](https://selector.sarsoo.xyz/)
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[Source Code](https://github.com/Sarsoo/Selector)
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---
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# [Listening Engineering](/posts/listening-analysis)
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`Python [scikit-learn, Jupyter]`
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__Spotify__ and __Last.fm__ are two powerful platforms for music data and consumption.
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I wanted to explore what insights could be found in my 3 years of __Last.fm__ scrobbles when augmented with __Spotify__ data. Ideally, I also wanted to be able to apply the intelligence to the __Mixonomer__ playlist pipeline.
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__Spotify__ provides audio features for the tracks on its platform. These features describe a number of qualities for the tracks including how much energy it has and how vocal it is. I investigated whether the set of audio features for my larger genre playlists could be used to classify tracks by genre.
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[Read More](/posts/listening-analysis)
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---
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# Signal Processing
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Throughout my studies I found myself particularly interested in the signal processing and AI modules, these have included:
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- Computer Vision and Pattern Recognition
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- Visual Search Report
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- Robotics
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- ROS Labs
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- Speech & Audio Processing & Recognition
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- Linear Predictive Speech Synthesiser
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- Hidden Markov Model Training
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- Image Processing & Deep Learning
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- CNN Training Coursework
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- AI & AI Programming
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- Shallow MLP Coursework
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[Posts](/posts)
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[Coursework Code](https://github.com/Sarsoo?tab=repositories&q=coursework)
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---
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I've been coding for 8 years and I now work as a software engineer in fintech. Day-to-day this is in [__C#__](/holo/) and [__TypeScript__](/mixonomer) but I also like working with [__Python__](/mixonomer) and [__Rust__](https://github.com/Sarsoo?tab=repositories&q=&type=&language=rust&sort=). I keep all of my projects on [__GitHub__](http://github.com/sarsoo).
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Alongside development I also enjoy working on infrastructure, I have 5 years experience using __Linux__ and managing networks. I have experience working with cloud technologies – from [__virtual machines__](/holo), [__web server PaaS__](/mixonomer) and [__serverless functions__](/mixonomer) to [__NoSQL__](/mixonomer), Big Data SQL and [__pub/sub messaging__](/mixonomer). Much of this experience was gained during my [__Mixonomer__](/mixonomer) project and during my __Disney__ internship. As part of my [dissertation](/holo#research), I used a global cluster of virtual machines as an environment to measure and experiment with holographic video QoS over long distances.
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At university, I was particularly interested in the software side of the field including modules in __programming__, __signal processing__ and __AI__. I also took a set of modules in __semiconductors__ and __nanoscience__.
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[Posts](/posts)
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---
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# Awards
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Throughout my time at university, I earned multiple awards for academic achievement
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- ___Dean’s List for Academic Achievement___ (2018)
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- Awarded for overall academic performance as part of my international exchange program with the [__California State University, Los Angeles__](https://www.calstatela.edu/)
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- ___Lumentum Award___ (2020)
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- Awarded for achieving the __highest mark__ in my third year __Semiconductors & Optoelectronics__ module
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- ___Atkins Best Oral Presentation – 2nd Prize___ (2021)
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- Awarded for giving second best oral presentation for ___multi-disciplinary design project___. Project involved designing a fully renewable ship and depot to repair sub-sea fibre optic cables. |