websitePrompt

Please a webpage using CSS with the following information. Create a single html document.
please make the website style vibrant, colorful and lively but formal. Correct any typo or grammatical errors, but do not abbreviate the information in the text.
All images (including thumbnails) should be no larger than 1/3 of the screen.
1. title is "John Usher's Homepage". Put this in the center in large black Courier font. The text should text highlighted with color #00ffc8.
Beneath this title, on a new line, (add a line break) in smaller font, put this text summary in italics: a short 5-15 word summary of this webpage, summarizing the talents of the individual (John Usher).
This text summary should be chosen at random from 10 possible sentences that have been pre-saved. 
Such an example (which you should include) should be ">20 years experience creating amazing new audio technologies"
 (put this font also in black font, with text highlighted with color #00ffc8.)
 This text summary should be randomized when the page is re-loaded.
 Below the text summary is this image: https://github.com/johnusher/johnusher.github.io/blob/main/images/drjhon_banner.png?raw=true this should strech to 75% of the width of the page.
 2. below put my email address: "yesjhon-at-gmail.com" with a hyper-linnk to the email address yesjhon-at-gmail.com
3. link to linkedin page: https://www.linkedin.com/in/drjohnusher/ (put an arrow going left to right  before the text to show it is a link)
4. link to github: https://github.com/johnusher (put an arrow going left to right before the text to show it is a link)


There are then 3 sections in this order
Section 1: "Projects:"
Section 2: "Experience:"
Section 3: "Specialties:"
This text should have black color.

At the bottom of the page, in a footer, give this text: "This webpage was automatically generated using Chat-GPT-4 and this prompt:" (link to this file: https://github.com/johnusher/johnusher.github.io/blob/main/websitePrompt)

6. Under the title of "Projects", show the following headings (these should all be centered int he page- the image and text should be center-justified).

6.1 Title = "Loudspeaker design:" Subtitle: "Carbon fibre active loudspeaker". Show a clickable thumbnail link to this video: https://www.youtube.com/watch?v=Stkl8KlzxmA

6.2. Title = "Spatial audio pickup for beamforming:". Show a clickable thumbnail link to this video: https://www.youtube.com/watch?v=L2qY9SO5M2M

6.3: Title = "Hearables and TWS Truly Wireless smart earphone design:". Show a clickable thumbnail link to this: https://myemail.constantcontact.com/The-Audio-Voice-160--Challenges-and-Solutions-for-True-Wireless-Stereo-Earphones-.html?soid=1104292817535&aid=lKsduyTab8s The thumbnail should show this image: https://techcrunch.com/wp-content/uploads/2015/08/screen-shot-2015-08-27-at-11-39-57-am.png?w=598
 
6.4: Title = "Various fun projects"
Subtitle 1:  "Control of lights using voice with Whisper-API and Chat-GPT API to generate Arduino code:". Show a clickable thumbnail link to https://github.com/johnusher/light-chat (Thumbnail link should be this image: https://logowik.com/content/uploads/images/346_raspberry_pi_logo.jpg)
Subtitle 2: "Teaching kids to spell. A "magic" wand with IMU and GPS can detect the letter being spelt, using a custom CNN neural-net". Show a clickable thumbnail link to https://github.com/johnusher/priWand. Image is this: https://www.shutterstock.com/shutterstock/photos/2051644397/display_1500/stock-vector-magic-wand-with-golden-neon-glowing-shiny-spiral-trail-vector-illustration-2051644397.jpg
Subtitle 3: "Determining a guitar chord using a single microphone with low-latency (<20 ms), ~90% reliable. This was a supervised learning algorithm that uses a generative model, specifically Gaussian Mixture Models (GMM) trained on Mel-Frequency Cepstral Coefficients (MFCC) features, for audio classification." 

6.5: Title = "Science-fiction writing:" Show a clickable thumbnail link to https://github.com/johnusher/Node/blob/main/Node_280524.pdf (Thumbnail link should be this image:  https://github.com/johnusher/Node/blob/main/images/cov%20Hedge%20black.png?raw=true

5. Experience: 

John Usher is a humble (ish) pioneer of spatial audio enhancement algorithms for earphones and loudspeakers. He holds a B.Eng. in Electro-Acoustics from the University of Salford and a Ph.D. from McGill University, where he was advised by such Masters as: Dr. W.L. Martens; Prof. W. Woszczyk; Dr. J. Benesty and Prof. A. Bregman. 
The Ph.D. introduced and characterised a new loudspeaker audio upmixing system, further developed for multichannel cinema audio at IMM sound, and now part of Dolby Atmos (patent #8,335,330).

Dr Usher has conducted research at the acoustics department of Bang and Olufsen, the Audio DSP group at Philips Nat Lab, and has given talks at Google-X, University of Cambridge, McGill University, and NASA.

John is an expert and pioneer in the field of Hearables: wearable electronic devices relating to sound. 
In 2006 he joined a new company designing and building smart Hearables. At Techiya (originally Personics, then Techiya), he spent over 10 years conjuring a new earphone for hearing enhancement, augmented reality, hearing protection (dosimetry), music listening and voice communications: it was, arguably, the worlds first smart consumer earphone.

John then co-founded a loudspeaker company. He engineered an all-carbon fibre high-end active loudspeaker. The design used a novel waveguide to direct sound towards a listening position, reducing colouration from room effects. The sound experience was commended by illustrious industry critics and musicians.

He now works in the audio group at Synaptics, engineering next-generation telecommunications systems using classical DSP and the latest ML systems. 

He is a keen tinkerer of electronics projects using Arduino and Raspberry Pi.

Specialties: (This should be a list, but do not show the list bullet-points)
Audio enhancement with DSP and ML for noise reduction, dereverberation, AEC, automatic level control, spatial filtering. 
Audio ML using RNN, CNN, U-Nets in Pytorch and Tensorflow with Onnx export.
Psychoacoustics for loudspeakers and earphones: timbral, spatial fidelity optimization, situational awareness (passthrough).
Low-latency audio DSP algorithms: ANC, echo-cancelling, beamforming, filtering, sound detection/ classification, sub-band processing, noise reduction, spatial filtering. 
Dosimetry. 
Room acoustics.
Spatial audio upmixing. 
Subjective sound quality evaluation for music and speech.
Code: Matlab, C, Python,  assembly, Go.
Patent drafting: 50+ patents on audio DSP.
Excellent communication and independent project management skills.
Leet Prompt Mashing.