̽��ֱ�� of Cambridge - Aalto ̽��ֱ��

Artificial intelligence powers record-breaking all-in-one miniature spectrometers

sc604 — Thu, 20 Oct 2022 18:00:00 +0000

We see light and colours around us every day. However, to analyse the information it carries, we must analyse light using spectrometers, in the lab. These devices detect sparkles and substances that our eyes would otherwise not notice.

Now, an international team of researchers, including the ̽��ֱ�� of Cambridge, have designed a miniaturised spectrometer that breaks all current resolution records, and does so in a much smaller package, thanks to computational programmes and artificial intelligence.

̽��ֱ��new miniaturised devices could be used in a broad range of sectors, from checking the quality of food to analysing starlight or detecting faint clues of life in outer space. ̽��ֱ��results are reported in the journal Science.

Traditionally, spectrometers rely on bulky components to filter and disperse light. Modern approaches simplify these components to shrink footprints, but still suffer from limited resolution and bandwidth. Additionally, traditional spectrometers are heavy and take up extraordinary amounts of space, which limits their applications in portable and mobile devices.

To tackle these problems, and shrink the size of the system, researchers have coupled layered materials with artificial intelligence algorithms. ̽��ֱ��result is an all-in-one spectrometer thousands of times smaller than current commercial systems. At the same time, it offers performance comparable to benchtop systems. In other words, these new spectrometers will provide portable alternatives to uncover otherwise invisible information, without even going into the lab.

“We eliminate the need for detector arrays, dispersive components, and filters. It’s an all-in-one, miniaturised device that could revolutionise this field,” said Dr Hoon Hahn Yoon, from Aalto ̽��ֱ�� in Finland, first author of the paper. This spectrometer-on-chip technology is expected to offer high performance and new usability across science and industry.

̽��ֱ��detector uses van der Waals heterostructures – a ‘sandwich’ of different ingredients, including graphene, molybdenum disulfide, and tungsten diselenide. Different combinations of material components enable light detection beyond the visible spectrum, as far as the near-infrared region. This means the spectrometer detects more than just colour, enabling applications such as chemical analysis and night vision.

“We detect a continuum spectrum of light, opening a world of possibilities in a myriad of markets,” said Yoon. “Exploring other material combinations could uncover further functionalities, including even broader hyperspectral detection and improved resolution.”

Artificial intelligence is a key aspect of these devices, commonly called ‘computational’ spectrometers. This technology compensates for the inherent noise increase that inevitably occurs when the optical component is wholly removed.

“We were able to use mathematical algorithms to successfully reconstruct the signals and spectra, it’s a profound and transformative technological leap,” said lead author Professor Zhipei Sun, also from Aalto ̽��ֱ��, and a former member of Cambridge’s Department of Engineering. “ ̽��ֱ��current design is just a proof-of-concept. More advanced algorithms, as well as different combinations of materials, could soon provide even better miniaturised spectrometers.”

Spectrometers are used for toxin detection in food and cosmetics, cancer imaging, and in spacecraft – including the James Webb Space Telescope. And they will soon become more common thanks to the development and advancement of technologies such as the Internet of Things and Industry 4.0.

̽��ֱ��detection of light – and the full analysis of spectroscopic information – has applications in sensing, surveillance, smart agriculture, and more. Among the most promising applications for miniaturised spectrometers are chemical and biochemical analysis, thanks to the capabilities of the devices to detect light in the infrared wavelength range.

̽��ֱ��new devices could be incorporated into instruments like drones, mobile phones, and lab-on-a-chip platforms, which can carry out several experiments in a single integrated circuit. ̽��ֱ��latter also opens up opportunities in healthcare. In this field, spectrometers and light-detectors are already key components of imaging and diagnostic systems – the new miniaturised devices could enable the simultaneous visualisation and detection of ‘chemical fingerprints’, leading to possibilities in the biomedical area.

“Our miniaturised spectrometers offer high spatial and spectral resolution at the micrometre and nanometre scales, which is particularly exciting for responsive bio-implants and innovative imaging techniques,” said co-author Professor Tawfique Hasan, from the Cambridge Graphene Centre.

This technology has huge potential for scalability and integration, thanks to its compatibility with well-established industrial processes. It could open up the future for the next generation of smartphone cameras that evolve into hyperspectral cameras that conventional colour cameras cannot do. Researchers hope their contribution is a stepping stone towards the development of more advanced computational spectrometers, with record-breaking accuracy and resolution. This example, they say, is just the first of many.

Reference:
Hoon Hahn Yoon et al. ‘Miniaturized Spectrometers with a Tunable van der Waals Junction.’ Science (2022). DOI: 10.1126/science.add8544.

Using Artificial Intelligence (AI) to replace optical and mechanical components, researchers have designed a tiny spectrometer that breaks all current resolution records.

Suvi-Tuuli Akkanen, Mikko Turunen, Vincent Pelgrin. Aalto ̽��ֱ��.

On-chip spectrometer on a fingertip

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What makes a faster typist?

sc604 — Thu, 05 Apr 2018 08:31:58 +0000

̽��ֱ��data was collected by researchers from Aalto ̽��ֱ�� in Finland and the ̽��ֱ�� of Cambridge. Volunteers from over 200 countries took the typing test, which is freely available online. Participants were asked to transcribe randomised sentences, and their accuracy and speed were assessed by the researchers.

Unsurprisingly, the researchers found that faster typists make fewer mistakes. However, they also found that the fastest typists also performed between 40 and 70 percent of keystrokes using rollover typing, in which the next key is pressed down before the previous key is lifted. ̽��ֱ��strategy is well-known in the gaming community but has not been observed in a typing study. ̽��ֱ��results will be presented later this month at the ACM CHI Conference on Human Factors in Computing Systems in Montréal.

“Crowdsourcing experiments that allow us to analyse how people interact with computers on a large scale are instrumental for identifying solution principles for the design of next-generation user interfaces,” said study co-author Dr Per Ola Kristensson from Cambridge’s Department of Engineering.

Most of our knowledge of how people type is based on studies from the typewriter era. Now, decades after the typewriter was replaced by computers, people make different types of mistakes. For example, errors where one letter is replaced by another are now more common, whereas in the typewriter era typists often added or omitted characters.

Another difference is that modern users use their hands differently. “Modern keyboards allow us to type keys with different fingers of the same hand with much less force than what was possible with typewriters,” said co-author Anna Feit from Aalto ̽��ֱ��. “This partially explains why self-taught typists using fewer than ten fingers can be as fast as touch typists, which was probably not the case in the typewriter era.”

̽��ֱ��average user in the study typed 52 words per minute, much slower than the professionally trained typists in the 70s and 80s, who typically reached 60-90 words per minute. However, performance varied largely. “ ̽��ֱ��fastest users in our study typed 120 words per minute, which is amazing given that this is a controlled study with randomised phrases,” said co-author Dr Antti Oulasvirta, also from Aalto. “Many informal tests allow users to practice the sentences, resulting in unrealistically high performance.”

̽��ֱ��researchers found that users who had previously taken a typing course actually had a similar typing behaviour as those who had never taken such a course, in terms of how fast they type, how they use their hands and the errors they make - even though they use fewer fingers.

̽��ֱ��researchers found that users display different typing styles, characterised by how they use their hands and fingers, the use of rollover, tapping speeds, and typing accuracy.

For example, some users could be classified as ‘’careless typists’’ who move their fingers quickly but have to correct many mistakes; and others as attentive error-free typists, who gain speed by moving hands and fingers in parallel, pressing the next key before the first one is released.

It is now possible to classify users’ typing behaviour based on the observed keystroke timings which does not require the storage of the text that users have typed. Such information can be useful for example for spell checkers, or to create new personalised training programmes for typing.

“You do not need to change to the touch typing system if you want to type faster,” said Feit. “A few simple exercises can help you to improve your own typing technique.”

̽��ֱ��anonymised dataset is available at the project homepage: http://userinterfaces.aalto.fi/136Mkeystrokes/

Reference:
Dhakal, V., Feit, A., Kristensson, P.O. and Oulasvirta, A. 2018. 'Observations on typing from 136 million keystrokes.' In Proceedings of the 36th ACM Conference on Human Factors in Computing Systems (CHI 2018). ACM Press.

Adapted from an Aalto ̽��ֱ�� press release.

̽��ֱ��largest-ever dataset on typing speeds and styles, based on 136 million keystrokes from 168,000 volunteers, finds that the fastest typists not only make fewer errors, but they often type the next key before the previous one has been released.

Crowdsourcing experiments that allow us to analyse how people interact with computers on a large scale are instrumental for identifying solution principles for the design of next-generation user interfaces.

Per Ola Kristensson

Photo by Cytonn Photography on Unsplash

Want to type faster?

Pay attention to errors, as they are costly to correct. Slow down to avoid them and you will be faster in the long run.
Learn to type without looking at fingers; your motor system will automatically pick up very fast ‘’trills’’ for frequently occurring letter combinations (“the”), which will speed up your typing. Being able to look at the screen while typing also allows you to quickly detect mistakes.
Practice rollover: use different fingers for successive letter keys instead of moving a single finger from one key to another. Then, when typing a letter with one finger, press the next one with the other finger.
Take an online typing test to track performance and identify weaknesses such as high error rates. Make sure that the test requires you to type new sentences so you do not over-practice the same text.
Dedicate time to practice deliberately. People may forget the good habits and relapse to less efficient ways of typing.

̽��ֱ��text in this work is licensed under a Creative Commons Attribution 4.0 International License. For image use please see separate credits above.

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