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A close-up of a silicon wafer during semi-conductor manufacturing

Chips and wafers are the integral components of a semiconductor device. Find out the differences between chip wafers and chips and understand their role in semiconductor manufacturing.

Chips and Wafers: What's the Difference?

YOLO Multi-Object Detection And Classification. Image credit: Ilija Mihajlovic

Embedded vision systems come with a host of benefits for both users and AI systems operations.


Embedded Vision AI Foundations

This is the 3rd and final article in a 3 part series featuring the applications of tinyML. The series introduces the concept of tinyML and explains some selected applications of the technology in various fields like sustainability, healthcare, and education.Education is the first step towards innovation. It is an integral part of personal development and equips the learners with the tools necessary to go ahead and solve the challenges faced by humanity. While tinyML is a great tool to develop pedagogical interventions for improving the quality of education in different streams, it is in itself a vast subject that every science and technology student must be familiar with.In our <a href="https://www.wevolver.com/article/applications-of-tinyml-in-healthcare-for-a-safer-future" target="_blank">previous article</a> about the applications of tinyML, we explained how it aids medical research and improves healthcare. This article continues the series by showcasing two more applications of tinyML for education.<h2 dir="ltr">Using tinyML for Neuroscience in K12 education: tinyML4STEM</h2>Anything and everything concerned with the central nervous system is a part of neuroscience. It is a multidisciplinary field that involves a scientific study of how the brain coordinates with the body and makes various body functions possible. The study is not just limited to humans but can also be extended to animals and even plants.Roughly 20% of the world’s population has suffered from a neurological disorder at some point in their lives, and none of the disorders have a cure yet. The only way to make new discoveries in the domain is by promoting research and education.It is also interesting to note that the most fundamental unit of Machine Learning (ML) is called a neuron and the whole concept and modelling of Artificial Intelligence (AI) based systems came from neuroscience. This makes it essential to talk about the specialised subject with the students of computer science to make them understand the underlying principles of the technology.But the problem with neuroscience education has always been the affordability and availability of experiment kits. Unlike many other Science Technology Engineering Management (STEM) fields, the apparatus for performing neuroscience experiments can not be acquired easily by schools and colleges. Graduate students opting for a specialisation in the area are the only ones to get their hands on these experiment kits.Greg Gage and Tim Marzullo realised this problem back in 2008 when they were pursuing PhD at the University of Michigan. The duo tried explaining neuroscience concepts to school children but couldn’t do so without showcasing practicals. There was no way to bring expensive graduate college equipment to schools. To solve this issue and make neuro-science kits affordable and readily available, Greg and Tim founded Backyard Brains.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjQzMjkwNDUyMjI0LWltYWdlNy5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 300px;" class="fr-fic fr-dib" alt="EMG-signals-backyard-brains">Illustrations like these can help kids grasp even complex concepts easily. Source: Backyard BrainsThe organisation today makes low-cost neuroscience trainers, DIY kits, tools, software, conducts workshops and a lot more to democratise neuroscience education.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjQzMjkwNTI1MDYyLWltYWdlMS5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 300px;" class="fr-fic fr-dib" alt="neuroscience-experiment-kits-backyard-brains">Neuroscience trainer kits by Backyard Brains are great alternatives for schools to introduce the students to the field. Source: Backyard BrainsNeuroscience signals like Electromyogram (EMG), Electrooculogram (EOG), Electrocardiogram (EKG), etc., can be used to get various cues about what is going on inside the body.When neurons fire electrical impulses from the motor cortex, the signals flow through the spinal cord to the parts of the body involved in the action. By putting small electrodes on the skin near the areas where the signals can be picked up, it is possible to observe them via an EMG. The impulses being electrical in nature can be amplified as a single-dimensional signal and visualised easily. These single-dimensional signals are beautiful for ML and are also quite easy to be analysed.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjQzMjkwNjI0MDcwLWltYWdlNC5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 300px;" class="fr-fic fr-dib" alt="visualising-neuroscience">A snapshot from tinyML talks where Greg demonstrates how EMG signals can be visualised with a low-cost training kit. Source: tinyML YouTubeThe patterns in these signals might not be as apparent to humans as they are to the tinyML algorithms, which can classify and map them with different human behaviours. An implementation of such a technique can be used to develop a brain-machine interface that can help make neuroprosthetics.&nbsp;EOG is another example of a neuro signal that measures the corneo-retinal standing potential of the eye. In other words, it helps understand eye movements. A trained TinyML system can be used to accurately detect eye blinking and movements by feeding the signal to the input.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjQzMjkwNjYzNTM5LWltYWdlOC5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 300px;" class="fr-fic fr-dib" alt="EOG-signals-detect-eye-blinking">The spikes that appear on the EOG are due to eye blinking. Source: tinyML YouTubeThen there’s EKG generated by the electrical impulses from the heart. For different people, the patterns of EKG are observed to be similar when looking at the same object. This can be used to predict if the person is telling the truth. In medical applications, signals can be used to check the functioning of the brain. A sample tone is played to the patient, and electric potential signals are observed. When the tone being played changes, the subconscious mind should ideally change the way it reacts, which should be evident in the electric potential signals.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjQzMjkxNTU2NjQ2LWltYWdlMy5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 300px;" class="fr-fic fr-dib" alt="hackerhand-copies-gestures-EKG-signals">‘Hackerhand’, a robotic arm mimicking hand movements based on EKG signals. Source: tinyML YouTubeIt is also interesting to note that the signals are generated slightly before someone is about to take action, making it possible for tinyML systems to be able to predict the behaviour in advance. A similar fun project has been developed at Backyard Brains.Readers can participate in many more such projects or share ideas by visiting <a href="https://backyardbrains.com/" target="_blank">Backyard Brains</a>.<h2 dir="ltr">TinyML Open Education Initiative: tinyMLedu</h2>Over the past two decades, ML has been through a journey from a purely academic and theoretical discipline to a widely spread technology in use. It won’t be an understatement to say that technology has touched almost every industry and is now ubiquitous technology. It is expected that the interest in the field will only grow from here, at least in the near foreseeable future. Given its amazing potential in solving some of the greatest challenges faced by humanity, it is important that more people are introduced to technology in a systematic way.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjQzMjkxNTk4OTQ2LWltYWdlNS5qcGciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 300px;" class="fr-fic fr-dib" alt="instructors-tinyML-harvard-google-edx">Instructors of the tinyML specialisationUnlike traditional ML, it is possible to run tinyML algorithms on low-cost and low-power embedded systems, which solves a major problem related to the unavailability of powerful hardware for learners. But the shortage of ML educators is still something that needs to be addressed.A joint initiative by Harvard and Google, developed as an academic and industry collaboration, aims to address the challenges by introducing tinyML to the masses with a Massive Open Online Course (MOOC) on edX.&nbsp;The courses that are a part of the tinyML specialisation go beyond teaching the students about the subject by diving deeper into the practical applications and industrial product development lifecycle. The next generation of students needs to understand the end-to-end full-stack development of tinyML projects to be able to gain a complete understanding of the subject.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjQzMjkxNjIwMzE4LWltYWdlOS5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 300px;" class="fr-fic fr-dib" alt="tinyML-specialisation-course-structure">A diagram providing an overview of topics explored in tinyML specialisation. Source: [3]The tinyML specialisation, jointly developed by Harvard and Google, is a set of self-paced learning modules that can be taken from anywhere. The specialisation includes four courses:<ol><li dir="ltr">Introduction to tinyML:&nbsp;The first course covers the basics of ML and embedded systems. Everything from the concept to gathering data and training the algorithms is explained.</li><li dir="ltr">Applications of tinyML: The second course builds on the foundation by explaining some parts of the code behind the most widely used tinyML applications. It also introduces the learners to the principles of keyword spotting, visual wake words, anomaly detection, and dataset engineering.</li><li dir="ltr">Deploying tinyML: The third course teaches how code is written, optimised, and deployed for embedded systems. This is where microcontrollers are introduced, and students can purchase a kit for a nominal price (US$49.99) to get hands-on experience.</li><li dir="ltr">Scaling tinyML [Optional]: Once the learners are familiar with the design, development and deployment of tinyML, the final course covers the more advanced aspects related to producing tinyML solutions for the masses.</li></ol>Each course includes several activities such as collabs, hands-on labs, quizzes, readings, assignments, tests, and discussion-forum participation.The key challenges for students taking up the tinyML specialisation can be limited hardware accessibility and the requirement of being familiar with programming. To address the challenges associated with hardware accessibility, open-source emulation platforms like <a href="https://renode.io/" target="_blank">renode.io</a> from Antmicro are being tested to allow learners to try out their code without the hardware. The other challenge can be solved by platforms like <a href="https://www.wevolver.com/profile/edge.impulse" target="_blank">Edge Impulse</a>, which are lowering the barriers to enable people with little to no programming knowledge to implement tinyML solutions.Anyone with interest in tinyML can visit <a href="https://www.edx.org/professional-certificate/harvardx-tiny-machine-learning" target="_blank">tinyML Professional Certificate | edX</a> and enrol for the course for free. The course material is available on <a href="https://github.com/tinyMLx/courseware" target="_blank">GitHub</a>.<h2 dir="ltr" id="isPasted">Conclusion</h2>Though the project kits or MOOCs are not a complete solution to train professional neuroscience researchers or tinyML solution architects, they offer a great place for beginners to start. The kits and courses are designed in such a way that the students can engage in fun activities while they experiment and learn new stuff. This makes the experience memorable and develops an interest in the subject, which could lead more people to choose the specialised fields and make new discoveries.&nbsp;This article concludes our tinyML applications series.About tinyML FoundationWith its workshops, webinars, expert talks, research conferences, competitions, and a plethora of other events, tinyML Foundation enables students, researchers, and industry personnel to come together and share their experiences about the technology. It is the incredibly collaborative nature of the community that allows it to advance rapidly.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjQzMjkxNjYyNjY4LWltYWdlMi5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 300px;" class="fr-fic fr-dib">The <a href="https://www.wevolver.com/article/tinyml-for-good-where-machine-learning-meets-edge-computing" target="_blank">introductory article</a> familiarised the readers with the concept of tinyML.The <a href="https://www.wevolver.com/article/tinyml" target="_blank">first article</a> explained some possibilities of tinyML applications in sustainable technologies.The <a href="https://www.wevolver.com/article/applications-of-tinyml-in-healthcare-for-a-safer-future/" target="_blank">second article</a> is about tinyML applications aiding healthcare and medical research.The <a href="https://www.wevolver.com/article/applications-of-tinyml-making-specialised-education-more-accessible" rel="noopener noreferrer" target="_blank">third and final article</a> shall cover the tinyML applications in pedagogy and education.<h2 dir="ltr">References</h2>[1] Marzullo TC, Gage GJ (2012) The SpikerBox: A Low Cost, Open-Source BioAmplifier for Increasing Public Participation in Neuroscience Inquiry. PLOS ONE 7(3): e30837. <a href="https://doi.org/10.1371/journal.pone.0030837" target="_blank">https://doi.org/10.1371/journal.pone.0030837</a>.[2] Gregory J. Gage, The Case for Neuroscience Research in the Classroom, Neuron, Volume 102, Issue 5, 2019, Pages 914-917, ISSN 0896-6273, <a href="https://doi.org/10.1016/j.neuron.2019.04.007" target="_blank">https://doi.org/10.1016/j.neuron.2019.04.007</a>.[3] Vijay J. Reddi et. al., Widening Access to Applied Machine Learning with TinyML, arXiv:2106.04008v2 [cs.LG], Jun 2021, [Online], Available from: <a href="https://arxiv.org/abs/2106.04008" target="_blank">https://arxiv.org/abs/2106.04008</a>.

In this article, we look at two tinyML projects for education. We show how Backyard Brains uses low-cost experiment kits to make neuroscience education more accessible. We also introduce our readers to a specialisation offered by Harvard & Google to help students learn tinyML like never before.

Applications of TinyML making specialised education more accessible

This is the 2nd article in a 3 part series featuring the applications of tinyML. The series introduces the concept of tinyML and explains some selected applications of the technology in various fields like sustainability, healthcare and education.By adopting digital advancements, healthcare providers are updating their services to improve them and make them accessible to more people. 5G networks, Augmented Reality (AR) &amp; Virtual Reality (VR), Wearables, Digital Twins, and many more technologies are enabling this digital transformation.One such technology with great potential in ensuring healthy lives and well-being for all is tinyML. The concept is a crossover between Artificial Intelligence (AI) and embedded systems that involves developing solutions that bring Machine Learning (ML) capabilities to edge devices. Benefits include privacy, affordability, low-power utilisation, the ability to work in remote locations, and a lot more.In our <a href="https://www.wevolver.com/article/tinyml" target="_blank">previous article</a> about the applications of tinyML, we explained how it unlocks new possibilities for sustainable development technologies. This article continues the series by showcasing two more applications of tinyML for medical research and improved healthcare.<h2 dir="ltr">MaRTiny: a tinyML solution for extreme heat sensing for urban climate science</h2>As a direct effect of global warming, summers are getting warmer. [1] This disruption of the natural balance poses a risk to all living entities on the planet. Higher temperatures are known to cause a degradation in the air quality and induce severe health-related issues like heat strokes. Looking at the data of weather-related fatalities in America, heat is the single most significant cause of weather-related deaths over the last 30 years, surpassing floods and storms. [2]But it’s not just the ambient temperature that causes discomfort. It’s the Mean Radiant Temperature (MRT) which accounts for the influence of all the surfaces in the surrounding that ultimately determines the occupant’s comfort. Often, the effect of roads, buildings and other entities has an adverse effect on the MRT, creating a complex problem intersecting health and urban infrastructure.To aid the collection of information related to the MRT, a tinyML solution called MaRTiny was developed by researchers from Arizona State University (ASU). It is a $200 low-cost mobile platform for bio-metrological sensing.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjQyNTE3MzYxNzgyLTE2NDI1MTczNjE3ODIucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" width="720" height="297" id="isPasted" class="fr-fic fr-dii" alt="MaRTiny-tech-stack">The tech stack of the MaRTiny platformThe list of sensors embedded on the device include:<ul><li dir="ltr">Ambient temperature sensor</li><li dir="ltr">Globe temperature sensor for sensing the radiant heat</li><li dir="ltr">Humidity sensor</li><li dir="ltr">Ultra Violet (UV) sensor</li><li dir="ltr">Anemometer for measuring wind speed and direction.</li></ul>The platform uses an <a href="https://www.wevolver.com/profile/arduino.pro" target="_blank">Arduino</a> Uno to collect the data and a NodeMCU to provide it with the electronic infrastructure required for wireless data communication. At the core of the system is the Nvidia Jetson Nano development board that provides MaRTiny with the power to run tinyML algorithms.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjQyNTE3NDcxMTAyLU5WSURJQV9KZXRzb25fTmFub19EZXZlbG9wZXJfS2l0Xyg0MDY1MDQyNTUwMykuanBnIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 300px;" class="fr-fic fr-dib" alt="nvidia-jetson-nano-development-board">The Nvidia Jetson Nano development board provides MaRTiny with image processing and ML capabilitiesJetson Nano, with its Camera Serial Interface (CSI), can connect with a camera for shadow detection, object detection and people-in-shade detection. Jetson’s ARM Cortex A57 MPcore, 128 <a href="https://www.wevolver.com/article/understanding-nvidia-cuda-cores-a-comprehensive-guide">CUDA core</a> Maxwell GPU coupled with 4 GB RAM is just enough to offer advanced ML capabilities on edge.The task of MRT estimation was formulated as a supervised learning problem by the inventors that required a lot of true data for training. This true data was collected with the help of a previously developed, larger and more expensive MRT detecting system going by the name of MaRTy. [3]&nbsp;<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjQyNTE3NTMzMzM5LTE1MDB4NTAwLmpwZyIsImVkaXRzIjp7InJlc2l6ZSI6eyJ3aWR0aCI6OTUwLCJmaXQiOiJjb3ZlciJ9fX0=" style="width: 300px;" class="fr-fic fr-dib" alt="MaRTiny-MaRTy">MaRTiny aims to offer similar functionality as that of MaRTy (in the image) at just 1% of the price. Image source: @ASUMaRTy, TwitterIt was observed that a Support Vector Machine (SVM) with a Radial Basis Function (RBF) kernel achieved the highest accuracy with the MaRTiny. The method being computationally light, offered an easy way of deployment on Jetson. To improve the estimations, both SVM and Artificial Neural Network (ANN) models were utilised and trained with data collected via sensors. [4]For privacy, the images taken by MaRTiny are not stored in the cloud or even in the device’s memory. It automatically discards personally identifiable information (PII) before reporting anything back to the servers, which is central to the idea of tinyML and a must-have feature for public deployment.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjQyNTE3NTc1MjI0LWltYWdlNy5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 300px;" class="fr-fic fr-dib" alt="MaRTiny-setup">The experimental setup of MaRTinyThe tinyML solution, MaRTiny, can provide crucial insights related to weather conditions and the utilisation of shades and space by the public. Multiple MaRTiny devices can be installed across the city to gather valuable data for architecture and town planning.<h2 dir="ltr">Pneumonia Detection using Embedded Machine Learning</h2>Pneumonia is a respiratory infection caused by viruses, bacterias and fungi. According to the World Health Organisation (WHO) data, it is estimated that as much as 22% of all deaths in children aged from 1 to 5 is due to pneumonia. Bacteria induced pneumonia can be treated with antibiotics and other low-cost and low-tech medical facilities if detected at the right time. [5]Chest X-ray, blood oxygen levels and Complete Blood Count (CBC) are used to detect pneumonia which is time-consuming. Doctors manually review the chest X-ray and decide if the person is infected, which is sometimes inaccurate and requires an automated solution with better accuracy. This is the idea behind the tinyML solution that we are about to discuss in this section.Arijit Das, a 15-year-old tinyML enthusiast, has developed a tinyML model using the <a href="https://www.wevolver.com/profile/edge.impulse" target="_blank">Edge Impulse</a> platform to detect pneumonia from chest X-rays. The model can be deployed on single board development platforms like Raspberry Pi 4, Sony Supersense and a few other devices. With just the main board, a camera shield and the Edge Impulse tinyML model, the tiny setup can detect pneumonia in under a minute which is phenomenal considering the fact that it takes anywhere between 1 to 4 days for detection by manual inspection.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjQyNTE3NzA0Nzk2LVJhc3BiZXJyeV9QaV80X01vZGVsX0JfLV9TaWRlLmpwZyIsImVkaXRzIjp7InJlc2l6ZSI6eyJ3aWR0aCI6OTUwLCJmaXQiOiJjb3ZlciJ9fX0=" style="width: 300px;" class="fr-fic fr-dib" alt="Raspberry-Pi-4">Credit card-sized single-board computers like Raspberry Pi come with enough compute power to run optimised tinyML algorithmsThe tinyML model doesn’t require internet connectivity to work and offers an accuracy of over 80%, which is far better compared to manual inspection. This can prove to be quite helpful in largely affected regions like Sub-Saharan Africa and South Asia, where a huge number of examinations need to be conducted related to the same disease.&nbsp;At the time of writing this article, the project is fully functional and has been clinically tested by over 10 doctors in a local survey conducted by the inventor. The project is available on <a href="https://github.com/Pneumonia-Detection-using-EdgeML" target="_blank">GitHub</a> for everyone to try, test and contribute. [6]<h2 dir="ltr">Conclusion</h2>It’s the third year into the pandemic, and the emphasis on healthcare research is more than ever. Introducing technologies that can work with little power and no network connectivity at an affordable price in healthcare can save several lives. Leveraging interdisciplinary technologies like tinyML is the way forward to a safer and healthier future.About tinyML FoundationWith its workshops, webinars, expert talks, research conferences, competitions, and a plethora of other events, tinyML Foundation enables students, researchers, and industry personnel to come together and share their experiences about the technology. It is the incredibly collaborative nature of the community that allows it to advance rapidly.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjQyNTE3NzY5MDkxLWV5SmlkV05yWlhRaU9pSjNaWFp2YkhabGNpMXdjbTlxWldOMExXbHRZV2RsY3lJc0ltdGxlU0k2SW1aeWIyRnNZUzh4TmpNMk9UZ3lNVEUwT1RRNExWUnBibmxOVEdadmNrZHZiMlFnS0RRd01EQWdlQ0EzTlRBZ2NIZ3BMbXB3WnlJc0ltVmthWFJ6SWpwN0luSmxjMmw2WlNJNmV5SjNhV1IwYUNJNk9UVXdMQ0ptYVhRaU9pSmpiM1psY2lKOWZYMD0ud2VicCIsImVkaXRzIjp7InJlc2l6ZSI6eyJ3aWR0aCI6OTUwLCJmaXQiOiJjb3ZlciJ9fX0=" style="width: 300px;" class="fr-fic fr-dib" alt="tinyML-banner"> The <a href="https://www.wevolver.com/article/tinyml-for-good-where-machine-learning-meets-edge-computing" target="_blank">introductory article</a> familiarised the readers with the concept of tinyML.The&nbsp;<a href="https://www.wevolver.com/article/tinyml" target="_blank">first article</a> explained some possibilities of tinyML applications in sustainable technologies.The <a href="https://www.wevolver.com/article/applications-of-tinyml-in-healthcare-for-a-safer-future/" rel="noopener noreferrer" target="_blank">second article</a> is about tinyML applications aiding healthcare and medical research.The third and final article shall cover the tinyML applications in&nbsp;pedagogy&nbsp;and education.<hr><h2>References</h2>[1] Extreme Weather Toolkits, Climate Central, [Online], Available from: &nbsp;<a href="https://medialibrary.climatecentral.org/extreme-weather-toolkits/extreme-heat" target="_blank">https://medialibrary.climatecentral.org/extreme-weather-toolkits/extreme-heat</a>[2] Weather Related Fatality and Injury Statistics, National Weather Service, [Online], Available from:&nbsp;<a href="https://www.weather.gov/hazstat/" target="_blank">https://www.weather.gov/hazstat/</a>[3] Ariane Middel, E. Scott Krayenhoff, ‘Micrometeorological determinants of pedestrian thermal exposure during record-breaking heat in Tempe, Arizona: Introducing the MaRTy observational platform’, Science of The Total Environment, Volume 687, 2019, Pages 137-151, ISSN 0048-9697, Available from:&nbsp;<a href="https://doi.org/10.1016/j.scitotenv.2019.06.085" target="_blank">https://doi.org/10.1016/j.scitotenv.2019.06.085</a>[4] Karthik K. Kulkarni, 2021, ‘Machine Learning and Vision Using Edge Devices for Multimodal Chatbots andBio-meteorological Sensing’, MS Thesis, Arizona State University, United States of America, [Online], Available from:&nbsp;<a href="https://keep.lib.asu.edu/_flysystem/fedora/c7/Kulkarni_asu_0010N_21269.pdf" target="_blank" data-lf-fd-inspected-kn9eq4roawkarlvp="true">https://keep.lib.asu.edu/_flysystem/fedora/c7/Kulkarni_asu_0010N_21269.pdf</a>[5] Pneumonia Facts Sheet, World Health Organisation, [Online], Available from:&nbsp;<a href="https://www.who.int/news-room/fact-sheets/detail/pneumonia" target="_blank">https://www.who.int/news-room/fact-sheets/detail/pneumonia</a>[6] Arijit Das, Pneumonia-Detection-using-EdgeML, GitHub, [Online], Available from:&nbsp;<a href="https://github.com/Pneumonia-Detection-using-EdgeML" target="_blank">https://github.com/Pneumonia-Detection-using-EdgeML</a>

In this article, we take a look at two tinyML projects related to healthcare. The first project helps gather Mean Radiant Temperature data outdoors to protect people from extreme heat, and the other one is a solution for affordable, accurate, & rapid detection of pneumonia.

Applications of TinyML in healthcare for a safer future

Changing weather patterns, rising sea levels, wildfires, droughts, depletion of natural resources are just some of the long-term consequences of unsustainable human activities. Indiscriminate exploitation of resources is accelerating global warming, and if preventive countermeasures are not taken immediately, the world may end up at a point of no return.TinyML is a field that aims to bring machine learning (ML) capabilities to low-power embedded systems. Its applications can be found from households to industries and everywhere in between. The idea of sustainability naturally links well with the tinyML as it encourages us to reduce our carbon footprint by executing ML algorithms on low-power devices.In our <a href="https://www.wevolver.com/article/tinyml-for-good-where-machine-learning-meets-edge-computing" rel="noopener noreferrer" target="_blank">previous article</a> about tinyML, we introduced our readers to the basics of the technology and how it is helping shape the embedded systems of the future. This article focuses on two interesting applications of tinyML for sustainable development.<h2>Groundwater Wells: The Untapped Edge Use Case</h2>Farming is the oldest and arguably the most crucial industry in the world. It employs around one-fourth of the world’s population and feeds billions of people. [1] Agriculture is also the largest use of fresh water in the world. Regrettably, it involves the largest wastage of water as well, making it incredibly inefficient. Most of the water used here is unregulated and unmonitored.Wastage of water has put 20% of the 39 million groundwater wells at the risk of running dry. [2] Once a well dries up, the only possible option is to dig a deeper one, but that can have a much worse long-term impact on the water table.<a href="https://niolabs.com" target="_blank">Niolabs</a> is a digital transformation and Internet of Things (IoT) solutions organisation that leverages IoT and tinyML in precision farming. Over the past couple of years, the organisation has achieved excellent results with the technology. Precision farming is the future of agriculture involving the collection and utilisation of real-time data to control the various irrigation automation systems. The result is lower consumption of water and energy while saving on operating costs. [3]<div class="fr-img-space-wrap"><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjQwMDkyNTAzODY3LXNtYXJ0X2Zhcm1pbmcuanBnIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 300px;" class="fr-fic fr-dib" alt="smart-farming">Precision farming with tinyML takes water conservation to the next level</div>For the implementation of the precision agriculture model, connecting all the devices to the network and passing data to the cloud is not always feasible, especially if a large number of data sources are involved. With thousands of plants being monitored for various environmental variables, the amount of data being generated is enormous and creates a security concern too.Using microcontrollers interfaced with moisture sensors and water control valves, it is possible to implement a simple automatic irrigation system that turns the irrigation system on or off depending on a static value of soil moisture levels. But not all plants require the same amount of moisture. Considering the environmental conditions like temperature, humidity, sunlight, and wind speed that have a direct effect on the water requirements of the plant can also be a challenge on the microcontroller programmed with simple if-else statements.This is where tinyML comes in. By implementing machine learning algorithms on the embedded controllers, it becomes possible to take into account the various environmental variables and dynamically adjust the water supply depending on the plant species and environmental conditions.The precision agriculture solution by Niolabs uses edge computing to enable each node to decide the amount of water required individually. The system uses an Xbee service that tracks the moisture levels near each watering zone and another service that allows users to provide their inputs about the watering requirements.&nbsp;Both the inputs and several other variables are combined and used as a reference to train the irrigation model, which keeps learning and improving with time—proof enough of the fact that combining general knowledge with ML can do wonders.The one thing that makes Niolabs’s implementation of a precision agriculture system unique is its ability to combine millions of seemingly unrelated data points into insightful and straightforward metrics, which can be accessed as a summary or detailed view at the fingertips of the users. The same system that maintains the plantation can also help with predictive maintenance. By using condition monitoring techniques, it can tell when its components might fail.Niolabs aims to come up with a revamped architecture of their application before March 2022. This is when the harvesting season begins, a perfect time to deploy and test the latest solutions. The plan is to create a repeatable tinyML solution that can be transferred to other farms and be scaled for use in a variety of different environments.<h2 dir="ltr">Ribbit Network: The world’s largest crowdsourced network of open-source, low-cost, Carbon Dioxide Gas Detection Sensors</h2>If we take a look at the long-term trend of worldwide greenhouse gas emissions, it is easy to note that the figures have shown constant growth in the past three decades [4]. There is a massive uptrend that continues to get steeper with each passing year. To make informed decisions and new policies regarding climate change, stakeholders and government organisations need accurate data.It is easy to assume that the data that is used presently is sufficient to make critical decisions, but that is not the case. The reference estimations are mostly theoretical calculations or predictions based on indirect economic measures. For example, vehicular emissions are roughly calculated based on the overall kilometers traveled and average emission per vehicle.&nbsp;The measurements that are direct, such as the ones taken by climate tech satellites, prove to be exorbitantly expensive. A single Orbiting Carbon Observatory 2 (OCO2) satellite that makes space-based observations of atmospheric carbon dioxide cost NASA over $467 mn. [5]Ribbit network aims to fill this gap by combining the economics with actual observations to reduce the uncertainties in data collection. Ribbit network takes over the challenge of building the map of emissions and concentration of carbon in the air with the help of tiny and affordable IoT sensors. They call them the ‘Frog’ sensors. The sensors get their name from frogs as these amphibians are hypersensitive to temperature and are the first ones to get affected due to climate change.<blockquote>Our primary interest in TinyML is based on decreasing power consumption. Running as much computing as we can on our IoT devices lets us minimize the carbon footprint that comes with big data center services. - Keenan Johnson</blockquote>The Frog sensors use a Non-Destructive Infra-Red (NDIR) sensor working on the principle of infrared (IR) spectroscopy. A typical NDIR sensor consists of an IR source, sample chamber and an IR detector. For measuring the presence of CO2, the air is allowed to flow inside the sample chamber. Depending on the concentration of CO2, some IR waves of a particular wavelength emitted by the source get absorbed. The difference between the emitted light and the light that makes it through the chamber gives conclusive evidence of the presence and quantitative estimation of the greenhouse gas in the air.<div class="fr-img-space-wrap"><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjM5ODA3MTYzMTk1LVVudGl0bGVkIGRlc2lnbi5qcGciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 300px;" class="fr-fic fr-dib" alt="ribbit-frog-carbon-dioxide-sensor-tinyml-block-diagram">Block diagram of the Frog sensor. Source: Ribbit network</div>The sensor is interfaced with a Raspberry Pi CM4, which takes care of everything right from powering the sensor to collecting and transmitting the data.The design of the frog sensors is entirely open-source which means, anyone can build their own version of the device by following the <a href="https://github.com/Ribbit-Network" target="_blank">official tutorial</a> from GitHub.&nbsp;<div class="fr-img-space-wrap"><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjM5ODA3MzAzMTE3LVVudGl0bGVkIGRlc2lnbiAoMSkuanBnIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 300px;" class="fr-fic fr-dib" alt="ribbit-frog-carbon-dioxide-sensor-tinyml">A closer look at the Frog sensor. Source: Ribbit network</div>Twenty-five alpha versions of frog sensors have been deployed across five countries. Beta versions will be deployed in a much larger number before the final rollout. The team is currently striving hard to solve problems related to determining sensor health, calibrations, the inclusion of wind data, and a lot more. With custom engineering solutions, they wish to reduce the cost of the sensor and power it with solar cells.<h2 dir="ltr">Conclusion</h2>It is not one, two, or three solutions that are going to solve the problem of climate change, but hundreds and thousands of innovative ideas collectively working together that can bring the revolution. That is what tinyML and its community members stand for.&nbsp;Grassroots innovations like the ones we showcased in this article can go long miles in meeting the global sustainable development goals, making the world a better place to live for future generations. Over the next couple of years, we expect tinyML to be leveraged far beyond than presently is.About tinyML FoundationWith its workshops, webinars, expert talks, research conferences, competitions, and a plethora of other events, tinyML Foundation enables students, researchers, and industry personnel to come together and share their experiences about the technology. It is the incredibly collaborative nature of the community that allows it to advance rapidly.<div class="fr-img-space-wrap"><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjM5ODA3ODQ1NDA0LVVudGl0bGVkIGRlc2lnbi5qcGciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 300px;" class="fr-fic fr-dib" alt="tiny-ml-foundation-banner"></div>The <a href="https://www.wevolver.com/article/tinyml-for-good-where-machine-learning-meets-edge-computing" target="_blank">introductory article</a> familiarised the readers with the concept of tinyML.The <a href="https://www.wevolver.com/article/tinyml-unlocks-new-possibilities-for-sustainable-development-technologies" target="_blank">first article</a> explained some possibilities of tinyML applications in sustainable technologies.The <a href="https://www.wevolver.com/article/applications-of-tinyml-in-healthcare-for-a-safer-future/" rel="noopener noreferrer" target="_blank">second article</a> is about tinyML applications aiding healthcare and medical research.The third and final article shall cover the tinyML applications in pedagogy and education.<hr><h2 dir="ltr" id="isPasted">References</h2>[1] The World Bank Data, Employment in agriculture (% of total employment) (modelled ILO estimate), 2021, [Online], Available from: <a href="https://data.worldbank.org/indicator/SL.AGR.EMPL.ZS" target="_blank">https://data.worldbank.org/indicator/SL.AGR.EMPL.ZS</a>[2] Harrison Tasoff, Gauging Groundwater, 23 April 2021, [Online], Available from: &nbsp;<a href="https://www.news.ucsb.edu/2021/020248/gauging-groundwater" target="_blank">https://www.news.ucsb.edu/2021/020248/gauging-groundwater</a>[3] Niolabs, Nio powers the only autonomous vineyard in the world, [Online], Available from: <a href="https://niolabs.com/case-studies/agriculture" target="_blank">https://niolabs.com/case-studies/agriculture</a>[4] United States Environment Protection Agency, Global Greenhouse Gas Emissions Data, [Online], Available from: <a href="https://www.epa.gov/ghgemissions/global-greenhouse-gas-emissions-data" target="_blank">https://www.epa.gov/ghgemissions/global-greenhouse-gas-emissions-data</a>[5] NASA, Orbiting Carbon Observatory-2 Launch Press Kit, July 2014, [Online], Available from: <a href="https://www.jpl.nasa.gov/news/press_kits/oco2-launch-press-kit.pdf" target="_blank">https://www.jpl.nasa.gov/news/press_kits/oco2-launch-press-kit.pdf</a>

In this article, we take a look at two tinyML projects that have the potential to make contributions towards sustainable development goals. While the first project is about revolutionising precision farming, the second one aims to create a network of low-cost sensors for mapping carbon emissions.

TinyML unlocks new possibilities for sustainable development technologies

This article is a part of our <a href="http://www.wevolver.com/student-program" target="_blank">University Technology Exposure Program</a>. The program aims to recognize and reward innovation from engineering students and researchers across the globe.&nbsp;When we think of smart fabrics, we usually associate them with wearable and fashion technology, such as clothing that can track your health and physical activity or gloves that can sense tactile information or your finger gestures. Since textiles are ubiquitous in our environments, other realms of textiles, such as flooring and upholstery, will also benefit from functional materials augmentation and technological integration.&nbsp;MIT Media Lab researchers have developed a large-scale seamless interactive carpet and established an interdisciplinary project that unites new materials, sensing technologies, and advanced digital fabrication with contemporary dance and music. Tapis Magique project explores the relationship between auditory and physical interaction through an interactive knitted electronic textile interface. The researchers aim to tease the idea of music or an immersive sonic environment that is driven in real-time by movements from dance and choreography and allow dancers to have their own agency over producing and controlling the music.&nbsp;<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjMyNzU1Nzg3NDA4LVRhcGlzTWFnaXF1ZTEgY29weSAyLkpQRyIsImVkaXRzIjp7InJlc2l6ZSI6eyJ3aWR0aCI6OTUwLCJmaXQiOiJjb3ZlciJ9fX0=" style="width: 766px;" class="fr-fic fr-dib">Credit Images: Irmandy Wicaksono/MIT Media LabThe project is motivated by traditional textile's extreme craftsmanship and artistry. Irmandy took some design inspiration from the cultural textiles of Indonesia, such as Javanese and Balinese Batik and Ikat. In these textiles, unique designs and patterns carry specific meanings and wishes. Complementing their traditional dance and music, they unitingly and harmoniously amplify a sense of community, identity, and expression. He hopes that by bringing functionality and interactivity to modern textiles, they will become even more appreciated, treasured, and personal and enhance our interaction with the environments and the digital world. Tapis Magique demonstrates the interplay between art and technology, highlighting the deep emotional link between contemporary textiles, dance, and music through the physical-digital connection.&nbsp;As textiles are highly soft, deformable, and palpable materials with a broad spectrum of textures, they are a great candidate for physical interfaces. Irmandy previously knitted a piano-patterned, multimodal musical cloth (<a href="https://www.digitaltrends.com/features/mit-knitted-keyboard/" target="_blank" rel="noopener noreferrer"></a><a href="https://www.digitaltrends.com/features/mit-knitted-keyboard/" rel="noopener noreferrer" target="_blank">KnittedKeyboard II</a>) that simultaneously responds to touch, pressure, stretch, and proximity. This 5-octave soft interface allows intimate and organic finger-fabric interaction. He also collaborated with a composer and keyboardist to demonstrate the multi-dimensionality of this e-textile instrument. With Tapis Magique, he is scaling the knitted e-textile technology and expanding its applications, incorporating footwork and body gestures.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjMyNzU2MzcyNTM0LVNjcmVlbiBTaG90IDIwMjEtMDktMjcgYXQgMTEuMjMuMTIgQU0ucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 766px;" class="fr-fic fr-dib">Knitting is an established textile manufacturing technique that has been pervasive in the garment and technical fabrics industry. &nbsp;In comparison to weaving, knitting has several advantages, including the ability to create intricate patterns and structures. Digital knitting machines now enable users to personalize and rapidly develop their textile design through a specialized visual programming environment and various functional and common yarns. It employs an array of needles or hooks that goes up and down to catch the yarns based on an instruction file. Each yarn is fed to the machine from a cone, passing through a tensioning mechanism towards a yarn carrier. These yarn carriers move sideways as the needles grab the yarn to form new loops. With advances in electronic materials and digital fabrication technologies, we can tune textile physical and at the resolution of a fiber, a loop structure, or a particle coating. These textiles can be fully customized from the micro to macro-scale and computationally designed and fabricated to form a seamless, heterogeneous, and multi-functional skin. This additive manufacturing process of machine-knitting enables the team to engineer patterns, colors, texture, thickness, elasticity, breathability, conductivity in discrete loops through fiber structures or material choices with as little waste as possible.&nbsp;<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjQ1ODE2NDYwNzM1LTE2NDU4MTY0NjA3MzQucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" id="isPasted" class="fr-fic fr-dii"> Tapis Magique is fabricated by knitting electrically-conducting yarns with other synthetic yarns, such as polyester, mink, thermoplastic, and luminous yarns, and fusing its multi-layer structures through thermoforming. The&nbsp;tapis&nbsp;design is composed of multi-layer knitted textiles. The top and bottom layers are orthogonal conductive line matrices knitted within a single operation using multi-material twisted yarns. The middle layer is a knitted piezoresistive textile, a pressure-sensitive layer that interfaces with the conductive matrices to create a sensing grid. The knitted piezoresistive textile is coated with conductive polymer called polypyrrole (PPy). &nbsp;Since it is piezoresistive, the resistance of this fabric sensor changes in correlation to the applied force. Like how a force-sensing resistor (FSR) works, a higher pressure compresses the conducting molecules coated into the fabric. These molecules then form a network, allowing more current to flow and reducing the resistance around the contact area.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjMyNzU2NDU1MDcwLVRhcGlzTWFnaXF1ZTUgY29weS5qcGciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 766px;" class="fr-fic fr-dib">In Tapis Magique, the researchers carefully consider the textile design from the yarn choices, textures, mechanical properties, to the aesthetics. The dense geometrical patterns of the stars scattered around the brushstroke details in the&nbsp;tapis&nbsp;represent 1800 pressure-sensing pixels and are inspired by the galactic space. Parametric design transformed these patterns into a 3-D spatial illusion to illustrate the multi-dimensionality of the sensor data. &nbsp;The furry textures from the synthetic mink yarns provide a soft tactility for physical feedback and give the tapis an intimate and comforting feel. The thermoplastic fibers were then steamed to melt the multi-layer knitted textiles into one rigid surface, giving it structural reinforcements. In addition, the outer-facing textile glows in the dark from the luminous yarns, bringing out the starry effects for night performance modes.&nbsp;The knitted conductive lines are connected to system hardware consisting of multiplexers, shift-registers, operational amplifiers, and a microcontroller that sequentially reads each pressure sensing pixel and sends it to a computer. These 60x30 pixels correspond to the pressure profile of the feet. They collectively generate continuous 3-D spatiotemporal sensor data correlated to the body posture, movements, and footwork. This data is then mapped into Musical Instrument Digital Interface (MIDI) streams to trigger and control discrete notes, continuous effects, and immersive soundscapes through science-inspired musical tools. MIDI is an industry-standard protocol that connects digital musical instruments, computers, tablets, and smartphones together and is used worldwide to create, produce, and perform music.<iframe src="https://player.vimeo.com/video/603497012" frameborder="0" allowfullscreen="" class="fr-draggable" style="width: 766px; height: 436px;" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true"></iframe> The team then designed several musical patches or pieces to invoke various emotions to inspire conversations between the choreographer and the instrument. Behind the scene, runs a digital modular synthesizer made of several patches, each for a different type of performance. The incoming stream of MIDI data is first fed into quantizer modules that align the notes to major, minor, and pentatonic scales, as well as mystic chords. The sounds are then generated by a collection of subtractive, additive, and granular synthesizers and are composed of discrete, melodic sounds with effects and continuously evolving soundscapes. "Venus Sunrise," one of the performance pieces demonstrated by the team, as shown in the video, presents a metaphorical celestial sound of the universe as the dancer is twirling around the stars, traveling through space and time. The team is now exploring other musical patches with different genres and ambiances.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjMyNzU2NzA2OTIyLVRhcGlzTWFnaXF1ZTggY29weSAzLkpQRyIsImVkaXRzIjp7InJlc2l6ZSI6eyJ3aWR0aCI6OTUwLCJmaXQiOiJjb3ZlciJ9fX0=" style="width: 753px;" class="fr-fic fr-dib">Real-time audio system connected to Tapis Magique<hr> Tapis Magique&nbsp;provides a canvas for dancers and sound artists to program, compose, perform, and even collaborate with their own choreomusical pieces. It could also create an auditory-gestural synesthetic experience that invites and encourages the audiences to move on the carpet, expressing themselves through dance and evokes the bi-directional, intimate relationship between two complex art forms of dance and music.&nbsp;Irmandy Wicaksono, an MIT PhD student from the Responsive Environments Group, MIT Media Lab led the project and was involved in the design, fabrication, and development of Tapis Magique with the advice of MIT Professor of Media Arts and Sciences Joe Paradiso. He collaborated with MIT PhD student and sound artist Don Derek Haddad and dancer Loni Landon to demonstrate this interactive e-textile carpet and produce several musical and performance pieces.&nbsp;<h2 dir="ltr" id="isPasted">About the University Technology Exposure Program 2022</h2>Wevolver, in partnership with <a href="https://mouser.com/" rel="nofollow" target="_blank">Mouser Electronics</a> and&nbsp;<a href="https://www.ansys.com/academic" rel="noopener noreferrer" target="_blank">Ansys</a>,&nbsp;is excited to announce the launch of the University Technology Exposure Program 2022. The program aims to recognize and reward innovation from engineering students and researchers across the globe. Learn more about the program <a href="http://www.wevolver.com/student-program" target="_blank">here</a>.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjQ1ODE2NTA2MjM0LWV5SmlkV05yWlhRaU9pSjNaWFp2YkhabGNpMXdjbTlxWldOMExXbHRZV2RsY3lJc0ltdGxlU0k2SW1aeWIyRnNZUzh4TmpRMU5UVXhNalEyTVRJNExVSnliM1ZuYUhRZ2RHOGdlVzkxSUdKNUlDZ3hLUzVxY0djaUxDSmxaR2wwY3lJNmV5SnlaWE5wZW1VaU9uc2lkMmxrZEdnaU9qazFNQ3dpWm1sMElqb2lZMjkyWlhJaWZYMTkud2VicCIsImVkaXRzIjp7InJlc2l6ZSI6eyJ3aWR0aCI6OTUwLCJmaXQiOiJjb3ZlciJ9fX0=" style="width: 300px;" class="fr-fic fr-dib">

Dancer Loni Landon performing with Tapis Magique

Tapis Magique is a pressure-sensitive, knitted electronic textile carpet that generates 3D sensor data based on body gestures and drives an immersive sonic environment in real time. 


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