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makers-muse · 6 months ago

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5 Atal Tinkering Lab projects that are transforming communities

Innovation has the power to change lives, and Atal Tinkering Labs (ATLs) are proving this every day. These hubs of creativity and experimentation, established in schools across India, are empowering students to solve real-world challenges in their communities. With access to cutting-edge technology and mentorship, students are building solutions that are not only ingenious but also impactful. Here are five inspiring ATL projects that are making a difference in communities across India.

1. Affordable Water Filtration System

Problem: Access to clean drinking water is a persistent challenge in many rural areas.

Solution: A group of students in Maharashtra designed a cost-effective water filtration system using locally available materials and microcontroller-based sensors. The system can detect impurities in water and purify it for safe consumption.

Impact: Villages in the region now have a reliable source of clean drinking water, reducing waterborne illnesses and improving overall health.

2. Smart Traffic Management System

Problem: Urban areas face increasing traffic congestion and accidents.

Solution: Students in Bengaluru developed a smart traffic management system using IoT technology and real-time data analysis. The system employs sensors and cameras to monitor traffic flow and adjust signals dynamically to reduce congestion.

Impact: The project has the potential to enhance road safety and streamline traffic in busy urban centers.

3. Low-Cost Prosthetic Hand

Problem: Prosthetic limbs are expensive and out of reach for many individuals in low-income communities.

Solution: An ATL team in Delhi created a low-cost prosthetic hand using 3D printing technology. The device is lightweight, customizable, and significantly cheaper than traditional prosthetics.

Impact: The project has restored mobility and independence to individuals who couldn’t afford prosthetic limbs, transforming their quality of life.

4. Solar-Powered Agricultural Tools

Problem: Farmers in remote areas often lack access to affordable and sustainable agricultural tools.

Solution: Students in Punjab developed solar-powered agricultural tools, including a water pump and seed planter. These tools are designed to be energy-efficient and suitable for small-scale farmers.

Impact: Farmers in the region now have access to affordable tools that reduce their reliance on expensive fuel and electricity, increasing productivity and profitability.

5. AI-Based Health Monitoring System

Problem: Rural areas face a shortage of healthcare professionals and facilities, leading to delayed diagnosis and treatment.

Solution: An ATL group in Tamil Nadu built an AI-based health monitoring system. This device measures vital parameters like heart rate and blood pressure, providing real-time health data that can be shared with doctors remotely.

Impact: The system has improved access to healthcare in underserved areas, enabling early detection of health issues and timely intervention.

Why ATLs Matter

These projects are a testament to the potential of Atal Tinkering Labs to inspire young minds and address pressing community challenges. By fostering innovation and providing hands-on learning opportunities, ATLs are not only preparing students for future careers but also instilling a sense of social responsibility.

Join the innovation movement!

Are you inspired by these stories? Act today encourage your school to set up an ATL or support existing labs in your area. Together, we can empower the next generation to innovate and create a brighter future for all. Start making a difference now! Contact us to learn how you can contribute.

#steam #steamteacher #steameducation #steamlab #steamclassroom #steamgames

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projecttopicsinnigeria · 8 years ago

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PROJECT TOPIC- CONSTRUCTION OF A MICROCONTROLLER BASED T-JUNCTION TRAFFIC LIGHT CONTROLLER

PROJECT TOPIC- CONSTRUCTION OF A MICROCONTROLLER BASED T-JUNCTION TRAFFIC LIGHT CONTROLLER ABSTRACT

T-junction traffic light controller is such a device that will play a significant role in controlling traffic at junctions, to ease the expected increased rush at such junctions and reduce to minimum disorderliness that may arise, as well as allowing the pedestrians a right of the way at…

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blog-markjohnson07 · 6 years ago

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Embedded Processor Market to Gain Higher Traction Piloted by Rising Application Benefits

An embedded processor is especially designed for handling the needs of an embedded system and to handle multiple processor in real time. A processor embedded into a system handles all the computational and logical operation of a computer. These processors are in the form of a computer chip that is embedded in various microcontrollers and microprocessors to control various electrical and mechanical systems. These processors are also equipped with features such as storing and retrieving data from the memory.

Get a Sample PDF of this Market Report at https://www.factomarketinsights.com/sample/753

With the emergence of enhanced technologies in medical devices such as wireless communication, sensors, ECG electrocardiogram, body area network (BAN) used for heart rate monitoring, and devices to monitor pulse rate, temperature, oxygen, and blood pressure, are fueling the growth of embedded processors in the healthcare industry vertical. All these equipment and devices are integrated with embedded processors for efficient working. For instance, devices integrated with embedded processors are used to identify cardiac abnormalities as against the conventional devices, which leads to the growth of embedded processors in this industry vertical.

Factors such as increasing space constraints in semiconductor wafers, rising demand for smart consumer electronics, and emerging usage of embedded processors in the automotive industry drive the embedded market growth globally. However, problems regarding deployment of embedded processors in harsh conditions hamper the market growth. Furthermore, increasing popularity of IoT, and growing usage of embedded processors in biomedical sector are expected to offer lucrative opportunities for market expansion.

The global embedded processor market is analyzed by type, application, and region. Based on type, the market is categorized into microprocessor, microcontrollers, digital signal processor, embedded FPGA, and others. On the basis of application, the market is divided into consumer electronics, automotive & transportation, industrial, healthcare, IT & telecom, aerospace & defense and others. Based on region, it is analyzed across North America, Europe, Asia-Pacific, and LAMEA along with their prominent countries.

The key players profiled in the report include NXP Semiconductors, Broadcom Corporation, STMicroelectronics, Intel Corporation, Infineon Technologies AG, Analog Devices Inc., Renesas Electronics, Microchip Technology Inc., Texas Instruments, and ON Semiconductor.

These key players have adopted strategies, such as product portfolio expansion, mergers & acquisitions, agreements, geographical expansion, and collaborations, to enhance their market penetration.

Read Detailed Index of full Research Study at https://www.factomarketinsights.com/report/753/embedded-processor-market-amr

KEY BENEFITS FOR STAKEHOLDERS • This study includes the analytical depiction of the global embedded processor market along with the current trends and future estimations to determine the imminent investment pockets. • The report presents information regarding the key drivers, restraints, and opportunities. • The current market is quantitatively analyzed from 2019 to 2026 to highlight the financial competency of the industry. • Porter’s five forces analysis illustrates the potency of the buyers and suppliers in the industry.

GLOBAL EMBEDDED PROCESSOR MARKET SEGMENTATION

BY TYPE: • Microprocessor • Microcontrollers • Digital Signal Processor • Embedded Field Programmable Gate Array (FPGA) • Others

BY APPLICATION: • Consumer Electronics • Automotive • Industrial • Healthcare • Others

BY REGION • North America o U.S. o Canada o Mexico • Europe o UK o Germany o France o Russia o Rest of Europe • Asia-Pacific o China o Japan o India o South Korea o Rest of Asia-Pacific • LAMEA o Latin America o Middle East o Africa

Ask our Expert if You Have a Query at https://www.factomarketinsights.com/enquiry/753

About Us

Facto Market Insights is one of the leading providers of market intelligence products and services. We offer reports on over 10+ industries and update our collection daily which helps our clients to access database of expert market insights on global industries, companies, products, and trends.

Our in-house research experts have a wealth of knowledge in their respective domains. With Facto Market Research, you always have the choice of getting customized report free of cost (upto 10%). Our support team will help you customize the report and scope as per your business needs. This ensures that you are making the right purchase decision.

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#Global Embedded Processor Market #Embedded Processor Market Report

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technotale · 2 years ago

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"Precision in Every Detail: Enhancing Measurements with MEMS Pressure Sensors"

MEMS (Microelectromechanical Systems) pressure sensors are miniaturized devices that measure and monitor pressure variations in a variety of applications. MEMS technology allows for the integration of mechanical structures, such as diaphragms and sensing elements, with electronics on a single chip, resulting in compact, high-performance pressure sensors.

Here are some key features and applications of MEMS pressure sensors:

Miniaturization and Sensitivity: MEMS pressure sensors are extremely small in size, typically ranging from a few millimeters to a few centimeters. Despite their small form factor, they exhibit high sensitivity and accuracy in detecting pressure changes. The miniature size enables their integration into space-constrained systems and applications.

Versatility and Range: MEMS pressure sensors are available in a wide range of pressure measurement capabilities, from very low pressures (e.g., a few Pascal) to high pressures (e.g., several megapascals). This versatility makes them suitable for various applications, including automotive, medical, industrial, consumer electronics, and aerospace industries.

Solid-State and Reliable Operation: MEMS pressure sensors operate based on the mechanical deflection of a diaphragm or structure under pressure. They do not contain moving parts, making them more reliable and resistant to shock, vibration, and wear compared to traditional pressure sensors with mechanical components.

Integration with Electronics: MEMS pressure sensors integrate sensing elements with electronics on the same chip, allowing for on-chip signal conditioning, amplification, and digital output. This integration simplifies the interface with microcontrollers or other electronic systems, making it easier to integrate pressure sensing into larger systems.

Low Power Consumption: MEMS pressure sensors are designed with low power consumption in mind, making them suitable for battery-powered devices and applications. Their efficient power usage extends the battery life of portable devices, while still providing accurate and real-time pressure measurements.

Cost-Effectiveness: MEMS technology enables the mass production of pressure sensors at a relatively low cost compared to traditional sensing technologies. This cost-effectiveness makes MEMS pressure sensors an attractive option for both high-volume consumer applications and cost-sensitive industrial applications.

Applications of MEMS pressure sensors include tire pressure monitoring systems (TPMS) in automotive, medical devices such as blood pressure monitors, environmental monitoring, industrial process control, HVAC systems, and many more. Their small size, high sensitivity, reliability, and integration capabilities make them a versatile and essential component in various systems that require accurate pressure measurement and monitoring.

Read more @ https://techinforite.blogspot.com/2023/06/sensing-world-exploring-versatility-of.html

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industrytrendsnews · 3 years ago

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WSN Market Analysis Report, Future Plans, Business Distribution, Application and Outlook | Impact of COVID-19

Multiple Factors to Boost Market Growth

One of the driving components of the wireless sensor network (WSN) market is the growing usage of computerized frameworks in various modern sectors. The use of automated frameworks in manufacturing organizations is rapidly rising, as it reduces human errors and provides higher proficiency, which will provide massive growth to the WSN market. Furthermore, the use of mechanized frameworks in agriculture, substance, and other ventures for conveying information from a long distance for the wellbeing of the representatives would help the WSN Market.

In addition, a few small-medium firms or SMBs, as well as large organizations, are quick to capitalize on the benefits of WSN solutions to improve the adaptability and productivity of their business measures. WSN has provided several benefits to SMBs, including lower activity costs, more noteworthy spryness and adaptability, increased income, and improved execution. This would result in an increase in WSN reception among SMBs.

Get Sample of Report @ https://www.marketresearchfuture.com/sample_request/1805

Market Segmentation

According to the Wireless Sensor Network Market the present market is segmented based on the type, connectivity, and end-user. Depending on the end-user type segment, the WSN Market has been fragmented into Automotive, Energy & Power, Aerospace & Defense, Healthcare, Oil & Gas, and Food & Beverage.

In terms of type segment, the global WSN market has been segregated into Position & Proximity, Blood Glucose Sensors, Accelerometers, Blood Oxygen Sensors, Carbon Monoxide Sensors, Electrocardiogram Sensors, Humidity Sensors, Image Sensors, Heart Rate Sensors, Temperature Sensors, Pressure, Ambient Light Sensors, Chemical Sensors, Flow Sensors, Level Sensors, and Inertial Measurement Units.

Based on the connectivity type segment, the Wireless Sensors Network Market has been segmented into Bluetooth Low Energy Bluetooth or Bluetooth Smart, Wireless Fidelity, Cellular Network, Near-Field Communication, Global Navigation Satellite System Module, Zigbee, Wireless Highway Addressable Remote Transducer, and Bluetooth WLAN.

Regional Analysis

As per the Wireless Sensor Network Market, the worldwide market is segmented into different geographical areas such as North America, Asia Pacific, Europe, Middle East & Africa, and Latin America.

The North American Wireless Sensor Network Market is dominating the worldwide market by creating significant market value in the review period. This region is growing upwards due to the high preferences of WSNs among the notable players (Honeywell International, Emerson Electric, and Texas Instruments)and the U.S. military in the region.

The growth of the North American market relies on the high adoption of smart factories, many industrial WSN manufacturers, and intelligent manufacturing. Moreover, the railroad industry of the region is highly focused on advanced monitoring and alerting, which is leveraging the Wireless Sensor Network Market in the forecast period.

Get complete Report @ https://www.marketresearchfuture.com/reports/wireless-sensor-network-market-1805

Industry News

In November 2019, STMicroelectronics declared STM32WB50 Value Line remote microcontrollers. These remote microcontrollers aim to serve costly associated gadgets required to help ZigBee® 3.0, Bluetooth® 5.0, or Open Thread.

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MRFR team have supreme objective to provide the optimum quality market research and intelligence services to our clients. Our market research studies by products, services, technologies, applications, end users, and market players for global, regional, and country level market segments, enable our clients to see more, know more, and do more, which help to answer all their most important questions.

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shashiemrf · 4 years ago

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The Ongoing Trend of Urbanization and Industrialization to Bolster Growth of the Wireless Sensor Network Market 2022

Market Overview

The worldwide Wireless Sensor Network Market is projected to grow the market value at a CAGR of 14% from 2020 to 2025. The WSN market has brought more scope for home mechanization, modern medical care, and others due to the sensors usages in different hardware gadgets and economic accessibility of sensors.

The global WSN market is thriving its growth because of the developing interest in robotized frameworks. Moreover, the computerized frameworks include a few advantages like better efficiency, less human mistake, and many others, which is propelling the growth of the WSN market. In addition, the presentation of advanced innovation like a web of things (IoT), artificial reasoning (A.I.), fifth-era remote innovation 5G, and others are also boosting up the Wireless Sensor Network Market Share during the review period. Further, the WSN market will be supported in the upcoming years because of the expanding autonomous vehicles, self-driving vehicles, and electric vehicles that utilize remote sensor networks.

Get Free Sample Report @ https://www.marketresearchfuture.com/sample_request/1805

However, the expansion of information interruptions can hinder market growth. Additionally, the worldwide market might experience challenges due to the less ability of the sensor to work and handle different tasks at a time.

Market Segmentation

According to the Wireless Sensor Network Market Trends, the present market is segmented based on the type, connectivity, and end-user. Depending on the end-user type segment, the WSN Market has been fragmented into Automotive, Energy & Power, Aerospace & Defense, Healthcare, Oil & Gas, and Food & Beverage.

Regional Analysis

As per the Wireless Sensor Network Market Trends, the worldwide market is segmented into different geographical areas such as North America, Asia Pacific, Europe, Middle East & Africa, and Latin America.

Industry News

Get Complete Report @ https://www.marketresearchfuture.com/reports/wireless-sensor-network-market-1805

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wolfliving · 5 years ago

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Deep learners for thermostats, the press release

FOR IMMEDIATE RELEASE: Friday, November 13, 2020

Contact: Abby Abazorius, MIT News Office [email protected]; 617.253.2709

System brings deep learning to “internet of things” devices

Advance could enable artificial intelligence on household appliances while enhancing data security and energy efficiency.

**Story/video: https://news.mit.edu/2020/iot-deep-learning-1113**

CAMBRIDGE, Mass. -- Deep learning is everywhere. This branch of artificial intelligence curates your social media and serves your Google search results. Soon, deep learning could also check your vitals or set your thermostat. MIT researchers have developed a system that could bring deep learning neural networks to new — and much smaller — places, like the tiny computer chips in wearable medical devices, household appliances, and the 250 billion other objects that constitute the “internet of things” (IoT).

The system, called MCUNet, designs compact neural networks that deliver unprecedented speed and accuracy for deep learning on IoT devices, despite limited memory and processing power. The technology could facilitate the expansion of the IoT universe while saving energy and improving data security.

The research will be presented at next month’s Conference on Neural Information Processing Systems. The lead author is Ji Lin, a PhD student in Song Han’s lab in MIT’s Department of Electrical Engineering and Computer Science. Co-authors include Han and Yujun Lin of MIT, Wei-Ming Chen of MIT and National University Taiwan, and John Cohn and Chuang Gan of the MIT-IBM Watson AI Lab.

The Internet of Things

The IoT was born in the early 1980s. Grad students at Carnegie Mellon University, including Mike Kazar ’78, connected a Cola-Cola machine to the internet. The group’s motivation was simple: laziness. They wanted to use their computers to confirm the machine was stocked before trekking from their office to make a purchase. It was the world’s first internet-connected appliance. “This was pretty much treated as the punchline of a joke,” says Kazar, now a Microsoft engineer. “No one expected billions of devices on the internet.”

Since that Coke machine, everyday objects have become increasingly networked into the growing IoT. That includes everything from wearable heart monitors to smart fridges that tell you when you’re low on milk. IoT devices often run on microcontrollers — simple computer chips with no operating system, minimal processing power, and less than one thousandth of the memory of a typical smartphone. So pattern-recognition tasks like deep learning are difficult to run locally on IoT devices. For complex analysis, IoT-collected data is often sent to the cloud, making it vulnerable to hacking.

“How do we deploy neural nets directly on these tiny devices? It’s a new research area that’s getting very hot,” says Han. “Companies like Google and ARM are all working in this direction.” Han is too.

With MCUNet, Han’s group codesigned two components needed for “tiny deep learning” — the operation of neural networks on microcontrollers. One component is TinyEngine, an inference engine that directs resource management, akin to an operating system. TinyEngine is optimized to run a particular neural network structure, which is selected by MCUNet’s other component: TinyNAS, a neural architecture search algorithm.

System-algorithm codesign

Designing a deep network for microcontrollers isn’t easy. Existing neural architecture search techniques start with a big pool of possible network structures based on a predefined template, then they gradually find the one with high accuracy and low cost. “It can work pretty well for GPUs or smartphones,” says Lin. “But it’s been difficult to directly apply these techniques to tiny microcontrollers, because they are too small.”

So Lin developed TinyNAS, a neural architecture search method that creates custom-sized networks. “We have a lot of microcontrollers that come with different power capacities and different memory sizes,” says Lin. “So we developed the algorithm [TinyNAS] to optimize the search space for different microcontrollers.” The customized nature of TinyNAS means it can generate compact neural networks with the best possible performance for a given microcontroller — with no unnecessary parameters. “Then we deliver the final, efficient model to the microcontroller,” say Lin.

To run that tiny neural network, a microcontroller also needs a lean inference engine. A typical inference engine carries some dead weight — instructions for tasks it may rarely run. The extra code poses no problem for a laptop or smartphone, but it could easily overwhelm a microcontroller. “It doesn’t have off-chip memory, and it doesn’t have a disk,” says Han. “Everything put together is just one megabyte of flash, so we have to really carefully manage such a small resource.” Cue TinyEngine.

The researchers developed their inference engine in conjunction with TinyNAS. TinyEngine generates the essential code necessary to run TinyNAS’ customized neural network. Any deadweight code is discarded, which cuts down on compile-time. “We keep only what we need,” says Han. “And since we designed the neural network, we know exactly what we need. That’s the advantage of system-algorithm codesign.” In the group’s tests of TinyEngine, the size of the compiled binary code was between 1.9 and five times smaller than comparable microcontroller inference engines from Google and ARM. TinyEngine also contains innovations that reduce runtime, including in-place depth-wise convolution, which cuts peak memory usage nearly in half. After codesigning TinyNAS and TinyEngine, Han’s team put MCUNet to the test.

MCUNet’s first challenge was image classification. The researchers used the ImageNet database to train the system with labeled images, then to test its ability to classify novel ones. On a commercial microcontroller they tested, MCUNet successfully classified 70.7 percent of the novel images — the previous state-of-the-art neural network and inference engine combo was just 54 percent accurate. “Even a 1 percent improvement is considered significant,” says Lin. “So this is a giant leap for microcontroller settings.”

The team found similar results in ImageNet tests of three other microcontrollers. And on both speed and accuracy, MCUNet beat the competition for audio and visual “wake-word” tasks, where a user initiates an interaction with a computer using vocal cues (think: “Hey, Siri”) or simply by entering a room. The experiments highlight MCUNet’s adaptability to numerous applications.

“Huge potential”

The promising test results give Han hope that it will become the new industry standard for microcontrollers. “It has huge potential,” he says.

The advance “extends the frontier of deep neural network design even farther into the computational domain of small energy-efficient microcontrollers,” says Kurt Keutzer, a computer scientist at the University of California at Berkeley, who was not involved in the work. He adds that MCUNet could “bring intelligent computer-vision capabilities to even the simplest kitchen appliances, or enable more intelligent motion sensors.”

MCUNet could also make IoT devices more secure. “A key advantage is preserving privacy,” says Han. “You don’t need to transmit the data to the cloud.”

Analyzing data locally reduces the risk of personal information being stolen — including personal health data. Han envisions smart watches with MCUNet that don’t just sense users’ heartbeat, blood pressure, and oxygen levels, but also analyze and help them understand that information. MCUNet could also bring deep learning to IoT devices in vehicles and rural areas with limited internet access.

Plus, MCUNet’s slim computing footprint translates into a slim carbon footprint. “Our big dream is for green AI,” says Han, adding that training a large neural network can burn carbon equivalent to the lifetime emissions of five cars. MCUNet on a microcontroller would require a small fraction of that energy. “Our end goal is to enable efficient, tiny AI with less computational resources, less human resources, and less data,” says Han.

###

Written by Daniel Ackerman

Paper: “MCUNet: Tiny Deep Learning on IoT Devices”

https://arxiv.org/pdf/2007.10319.pdf

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damiencordle · 5 years ago

Text

I Found This Interesting. Joshua Damien Cordle.

System brings deep learning to 'internet of things' devices

Advance could enable artificial intelligence on household appliances while enhancing data security and energy efficiency

Deep learning is everywhere. This branch of artificial intelligence curates your social media and serves your Google search results. Soon, deep learning could also check your vitals or set your thermostat. MIT researchers have developed a system that could bring deep learning neural networks to new -- and much smaller -- places, like the tiny computer chips in wearable medical devices, household appliances, and the 250 billion other objects that constitute the "internet of things" (IoT).

The research will be presented at next month's Conference on Neural Information Processing Systems. The lead author is Ji Lin, a PhD student in Song Han's lab in MIT's Department of Electrical Engineering and Computer Science. Co-authors include Han and Yujun Lin of MIT, Wei-Ming Chen of MIT and National University Taiwan, and John Cohn and Chuang Gan of the MIT-IBM Watson AI Lab.

The Internet of Things

The IoT was born in the early 1980s. Grad students at Carnegie Mellon University, including Mike Kazar '78, connected a Cola-Cola machine to the internet. The group's motivation was simple: laziness. They wanted to use their computers to confirm the machine was stocked before trekking from their office to make a purchase. It was the world's first internet-connected appliance. "This was pretty much treated as the punchline of a joke," says Kazar, now a Microsoft engineer. "No one expected billions of devices on the internet."

Since that Coke machine, everyday objects have become increasingly networked into the growing IoT. That includes everything from wearable heart monitors to smart fridges that tell you when you're low on milk. IoT devices often run on microcontrollers -- simple computer chips with no operating system, minimal processing power, and less than one thousandth of the memory of a typical smartphone. So pattern-recognition tasks like deep learning are difficult to run locally on IoT devices. For complex analysis, IoT-collected data is often sent to the cloud, making it vulnerable to hacking.

"How do we deploy neural nets directly on these tiny devices? It's a new research area that's getting very hot," says Han. "Companies like Google and ARM are all working in this direction." Han is too.

With MCUNet, Han's group codesigned two components needed for "tiny deep learning" -- the operation of neural networks on microcontrollers. One component is TinyEngine, an inference engine that directs resource management, akin to an operating system. TinyEngine is optimized to run a particular neural network structure, which is selected by MCUNet's other component: TinyNAS, a neural architecture search algorithm.

System-algorithm codesign

Designing a deep network for microcontrollers isn't easy. Existing neural architecture search techniques start with a big pool of possible network structures based on a predefined template, then they gradually find the one with high accuracy and low cost. While the method works, it's not the most efficient. "It can work pretty well for GPUs or smartphones," says Lin. "But it's been difficult to directly apply these techniques to tiny microcontrollers, because they are too small."

So Lin developed TinyNAS, a neural architecture search method that creates custom-sized networks. "We have a lot of microcontrollers that come with different power capacities and different memory sizes," says Lin. "So we developed the algorithm [TinyNAS] to optimize the search space for different microcontrollers." The customized nature of TinyNAS means it can generate compact neural networks with the best possible performance for a given microcontroller -- with no unnecessary parameters. "Then we deliver the final, efficient model to the microcontroller," say Lin.

To run that tiny neural network, a microcontroller also needs a lean inference engine. A typical inference engine carries some dead weight -- instructions for tasks it may rarely run. The extra code poses no problem for a laptop or smartphone, but it could easily overwhelm a microcontroller. "It doesn't have off-chip memory, and it doesn't have a disk," says Han. "Everything put together is just one megabyte of flash, so we have to really carefully manage such a small resource." Cue TinyEngine.

The researchers developed their inference engine in conjunction with TinyNAS. TinyEngine generates the essential code necessary to run TinyNAS' customized neural network. Any deadweight code is discarded, which cuts down on compile-time. "We keep only what we need," says Han. "And since we designed the neural network, we know exactly what we need. That's the advantage of system-algorithm codesign." In the group's tests of TinyEngine, the size of the compiled binary code was between 1.9 and five times smaller than comparable microcontroller inference engines from Google and ARM. TinyEngine also contains innovations that reduce runtime, including in-place depth-wise convolution, which cuts peak memory usage nearly in half. After codesigning TinyNAS and TinyEngine, Han's team put MCUNet to the test.

MCUNet's first challenge was image classification. The researchers used the ImageNet database to train the system with labeled images, then to test its ability to classify novel ones. On a commercial microcontroller they tested, MCUNet successfully classified 70.7 percent of the novel images -- the previous state-of-the-art neural network and inference engine combo was just 54 percent accurate. "Even a 1 percent improvement is considered significant," says Lin. "So this is a giant leap for microcontroller settings."

The team found similar results in ImageNet tests of three other microcontrollers. And on both speed and accuracy, MCUNet beat the competition for audio and visual "wake-word" tasks, where a user initiates an interaction with a computer using vocal cues (think: "Hey, Siri") or simply by entering a room. The experiments highlight MCUNet's adaptability to numerous applications.

"Huge potential"

The promising test results give Han hope that it will become the new industry standard for microcontrollers. "It has huge potential," he says.

The advance "extends the frontier of deep neural network design even farther into the computational domain of small energy-efficient microcontrollers," says Kurt Keutzer, a computer scientist at the University of California at Berkeley, who was not involved in the work. He adds that MCUNet could "bring intelligent computer-vision capabilities to even the simplest kitchen appliances, or enable more intelligent motion sensors."

MCUNet could also make IoT devices more secure. "A key advantage is preserving privacy," says Han. "You don't need to transmit the data to the cloud."

Analyzing data locally reduces the risk of personal information being stolen -- including personal health data. Han envisions smart watches with MCUNet that don't just sense users' heartbeat, blood pressure, and oxygen levels, but also analyze and help them understand that information. MCUNet could also bring deep learning to IoT devices in vehicles and rural areas with limited internet access.

Plus, MCUNet's slim computing footprint translates into a slim carbon footprint. "Our big dream is for green AI," says Han, adding that training a large neural network can burn carbon equivalent to the lifetime emissions of five cars. MCUNet on a microcontroller would require a small fraction of that energy. "Our end goal is to enable efficient, tiny AI with less computational resources, less human resources, and less data," says Han.

make a difference: sponsored opportunity

Story Source:

Materials provided by Massachusetts Institute of Technology. Original written by Daniel Ackerman. Note: Content may be edited for style and length.

Journal Reference:

Ji Lin, Wei-Ming Chen, Yujun Lin, John Cohn, Chuang Gan, Song Han. MCUNet: Tiny Deep Learning on IoT Devices. submitted to arXiv, 2020 [abstract]

#Joshua Damien Cordle #technology news

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mindthump · 5 years ago

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Why TinyML is a giant opportunity https://ift.tt/35QdGDS

The world is about to get a whole lot smarter.

As the new decade begins, we’re hearing predictions on everything from fully remote workforces to quantum computing. However, one emerging trend is scarcely mentioned on tech blogs – one that may be small in form but has the potential to be massive in implication. We’re talking about microcontrollers.

There are 250 billion microcontrollers in the world today. 28.1 billion units were sold in 2018 alone, and IC Insights forecasts annual shipment volume to grow to 38.2 billion by 2023.

Perhaps we are getting a bit ahead of ourselves though, because you may not know exactly what we mean by microcontrollers. A microcontroller is a small, special purpose computer dedicated to performing one task or program within a device. For example, a microcontroller in a television controls the channel selector and speaker system. It changes those systems when it receives input from the TV remote. Microcontrollers and the components they manage are collectively called embedded systems since they are embedded in the devices they control. Take a look around — these embedded systems are everywhere, in nearly any modern electronic device. Your office machines, cars, medical devices, and home appliances almost all certainly have microcontrollers in them.

With all the buzz about cloud computing, mobile device penetration, artificial intelligence, and the Internet of Things (IoT) over the past few years, these microcontrollers (and the embedded systems they power) have largely been underappreciated. This is about to change.

The strong growth in microcontroller sales in recent years has been largely driven by the broad tailwinds of the IoT. Microcontrollers facilitate automation and embedded control in electronic systems, as well as the connection of sensors and applications to the IoT. These handy little devices are also exceedingly cheap, with an average price of 60 cents per unit (and dropping). Although low in cost, the economic impact of what microcontrollers enable at the system level is massive, since the sensor data from the physical world is the lifeblood of digital transformation in industry. However, this is only part of the story.

A coalescence of several trends has made the microcontroller not just a conduit for implementing IoT applications but also a powerful, independent processing mechanism in its own right. In recent years, hardware advancements have made it possible for microcontrollers to perform calculations much faster. Improved hardware coupled with more efficient development standards have made it easier for developers to build programs on these devices. Perhaps the most important trend, though, has been the rise of tiny machine learning, or TinyML. It’s a technology we’ve been following since investing in a startup in this space.

Big potential

TinyML broadly encapsulates the field of machine learning technologies capable of performing on-device analytics of sensor data at extremely low power. Between hardware advancements and the TinyML community’s recent innovations in machine learning, it is now possible to run increasingly complex deep learning models (the foundation of most modern artificial intelligence applications) directly on microcontrollers. A quick glance under the hood shows this is fundamentally possible because deep learning models are compute-bound, meaning their efficiency is limited by the time it takes to complete a large number of arithmetic operations. Advancements in TinyML have made it possible to run these models on existing microcontroller hardware.

In other words, those 250 billion microcontrollers in our printers, TVs, cars, and pacemakers can now perform tasks that previously only our computers and smartphones could handle. All of our devices and appliances are getting smarter thanks to microcontrollers.

TinyML represents a collaborative effort between the embedded ultra-low power systems and machine learning communities, which traditionally have operated largely independently. This union has opened the floodgates for new and exciting applications of on-device machine learning. However, the knowledge that deep learning and microcontrollers are a perfect match has been pretty exclusive, hidden behind the walls of tech giants like Google and Apple. This becomes more obvious when you learn that this paradigm of running modified deep learning models on microcontrollers is responsible for the “Okay Google” and “Hey Siri,” functionality that has been around for years.

But why is it important that we be able to run these models on microcontrollers? Much of the sensor data generated today is discarded because of cost, bandwidth, or power constraints – or sometimes a combination of all three. For example, take an imagery micro-satellite. Such satellites are equipped with cameras capable of capturing high resolution images but are limited by the size and number of photos they can store and how often they can transmit those photos to Earth. As a result, such satellites have to store images at low resolution and at a low frame rate. What if we could use image detection models to save high resolution photos only if an object of interest (like a ship or weather pattern) was present in the image? While the computing resources on these micro-satellites have historically been too small to support image detection deep learning models, TinyML now makes this possible.

Another benefit of deploying deep learning models on microcontrollers is that microcontrollers use very little energy. Compared to systems that require either a direct connection to the power grid or frequent charges or replacement of the battery, a microcontroller can run an image recognition model continuously for a year with a single coin battery. Furthermore, since most embedded systems are not connected to the internet, these smart embedded systems can be deployed essentially anywhere. By enabling decision-making without continuous connectivity to the internet, the ability to deploy deep learning models on embedded systems creates an opportunity for completely new types of products.

Early TinyML applications

It’s easy to talk about applications in the abstract, but let’s narrow our focus to specific applications likely to be available in the coming years that would impact the way we work or live:

Mobility: If we apply TinyML to sensors ingesting real-time traffic data, we can use them to route traffic more effectively and reduce response times for emergency vehicles. Companies like Swim.AI use TinyML on streaming data to improve passenger safety and reduce congestion and emissions through efficient routing.

Smart factory: In the manufacturing sector, TinyML can stop downtime due to equipment failure by enabling real-time decision. It can alert workers to perform preventative maintenance when necessary, based on equipment conditions.

Retail: By monitoring shelves in-store and sending immediate alerts as item quantities dwindle, TinyML can prevent items from becoming out of stock.

Agriculture: Farmers risk severe profit losses from animal illnesses. Data from livestock wearables that monitor health vitals like heart rate, blood pressure, temperature, etc. can help predict the onslaught of disease and epidemics.

Before TinyML goes mainstream …

As intriguing as TinyML may be, we are very much in the early stages, and we need to see a number of trends occur before it gets mainstream adoption.

Every successful ecosystem is built on engaged communities. A vibrant TinyML community will lead to faster innovation as it increases awareness and adoption. We need more investments in open-source projects supporting TinyML (like the work Google is doing around TensorFlow for broader machine learning), since open source allows each contributor to build on top of the work of others to create thorough and robust solutions.

Other core ecosystem participants and tools will also be necessary:

Chipset manufacturers and platforms like Qualcomm, ST, and ETA Compute can work hand-in-hand with developers to ensure chipsets are ready for the intended applications, and that platform integrations are built to facilitate rapid application development.

Cloud players can invest in end-to-end optimized platform solutions that allow seamless exchange and processing of data between devices and the cloud.

Direct support is needed from device-level software infrastructure companies such as Memfault, which is trying to improve firmware reliability, and Argosy Labs, which is tackling data security and sharing on the device level. These kinds of changes give developers more control over software deployments with greater security from nearly any device.

Lifecycle TinyML tools need to be built that facilitate dataset management, algorithm development, and version management and that enhance the testing and deployment lifecycle.

However, innovators are ultimately what drives change. We need more machine learning experts who have the resources to challenge the status quo and make TinyML even more accessible. Pete Warden, head of the TensorFlow mobile team, has an ambitious task of building machine learning applications that run on a microcontroller for a year using only a hearing aid battery for power. We need more leaders like Pete to step up and lead breakthroughs to make TinyML a near-term reality.

In summary: TinyML is a giant opportunity that’s just beginning to emerge. Expect to see quite a bit of movement in this space over the next year or two.

[Find out about VentureBeat guest posts.]

TX Zhuo is General Partner at Fika Ventures.

Huston Collins is Senior Associate at Fika Ventures.

#IFTTT #Feedly #WOW #good to know #tech #hmmm #robot #science #future

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jigneshthanki-blog · 6 years ago

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The Vital Role of IoT in Heart Attack Detection and Heart Rate Monitoring

According to the Centers for Disease Control and Prevention, the leading cause of death among men and women is heart disease, accounting for 630,000 deaths in the United States alone each year. Unfortunately, this isn’t unique to the United States, with heart disease killing more than 1,500,000 Chinese each year, according to the International Conference on Knowledge-Based and Intelligent Information and Engineering Systems.

One of the main reasons why so many individuals die from heart attacks is because there is a short window of time between when the symptoms begin and when the individual can make it to a hospital. Since symptoms vary from one individual to the next, many are unaware of the early warning signs and delay seeking medical attention. In the few hours that are critical after a heart attack, if the individual cannot receive medical attention, the dying heart muscle will be permanently damaged due to a lack of oxygen

To overcome these delays, engineers are working on developing an IoT enabled heart attack detection system and heart rate monitor that will help alert individuals to seek out medical attention before an attack. The device is wearable and will continuously monitor the individual’s electrocardiogram (electrical activity of the heart) and notify them if there are elevated levels that would lead to myocardial infarction. The device will tell the user the exact location of the event, and it will be connected to the cloud to alert others via Wi-Fi.

How Does the Proposed IoT Solution Work?

The proposed system will be able to detect a heart attack by observing the user’s heart rate through an Internet of Things architecture.

The general architecture of most IoT solutions can be divided into three primary layers; the sensing layer, the transport layer, and the application layer. The sensing layer is what allows the patient’s vitals to be read, while the transport layer provides data transmission through transit points, and the application layer is installed at the end of the device. In this particular IoT solution, a pulse sensor, Arduino board, and Wi-Fi module are being used to create the system. The system’s architecture works as follows.

Once the system is in place and set up, the individual can set up a start point. This allows the individual to monitor their heartbeat and compare it to the set point. Once these limits are set, the system will monitor the heart rate of the individual continuously.

The pulse sensor is connected to an Arduino Uno microcontroller board, which has 14 digital input and output pins on it, 6 of which can be used to monitor PWM outputs.

The RX and TX pins on the board are used for communication between the Arduino board and the Wi-Fi module, LCD screen, and heart rate application. The Arduino microcontroller board has an operating voltage of 5-volts and uses 32KB of flash memory for code storage.

The pulse sensor being used is a “plug and play” heart rate sensor, which allows the individual to simply place their finger on it to take a reading. The sensor picks up the movement of blood through the finger and gives a numerical output of the heartbeat.

Once the pulse sensor crosses the set limit, the system will send out an alert about the heart rate to the individual. The system also allows you to set it to alert for low heartbeats.

What does Architecture Look Like?

At the beginning of the system, you have the pulse sensor which detects the heartbeat signal. The heartbeat signal is attached to the Arduino Uno microcontroller board. The Arduino Uno board is connected to the Wi-Fi module and the LCD screen. The Wi-Fi module is connected to the heart rate application, allowing for the data to be transported over the internet to individuals like caregivers and doctors.

How Are These IoT Solutions Being Used in HealthTech?

The heart attack detection and heart rate monitor aren’t the only use case of how IoT solutions are vital in our healthcare system. There are other applications of similar IoT enabled devices that can monitor heart disease, help the recovery process after a stroke, and even help heart condition patients live a normal life with IoT enabled pacemakers.

Heart Disease Monitoring:

Doctors can now keep track of a patient’s condition without needing them to come into the office as IoT solutions have been developed to measure blood sugar levels, cholesterol levels, and blood pressure levels. This information can be received in real-time through a heart disease monitoring device that transmits the data through the internet.

Stroke Recovery:

Wearable sensors have been developed to monitor an individual’s rehabilitation process. In previous iterations, patients would be bed-ridden and monitored with dozens of wires for the time that recovery was required. Now, the data can be systematized through a flexible and wearable sensor application that can be set to notify when goals are met, track recovery progress metrics and create alerts about key recovery indicators.

IoT Pacemakers:

A pacemaker is a small device that is placed in one’s chest to help control your heartbeat. It is used to help your heartbeat at regular intervals if you have an irregular heartbeat. An IoT enabled pacemaker could provide breakthrough information about a patient’s exact condition as real-time results can be transmitted to doctors and caregivers.

You can read also – “Medcheck” – An IoT based Healthcare Diagnostic Solution

What Are the Benefits of IoT in Healthcare?

Real-time monitoring can save lives during a medical emergency like heart failure, asthma attacks, and diabetic episodes (remote medical assistance).

IoT devices can be continuously connected and are an affordable solution for those who want to cut down on unnecessary doctor visits.

IoT devices can be used for research purposes since they collect a massive amount of data about an individual. This can speed up the rate at which cures, and better treatments can be found.

While there have been numerous heart attack detection methods introduced into our healthcare systems, most are time-consuming to create and expensive to manufacture. A proposed technique using an Arduino Uno microcontroller board is a more modern take that can be easy to manufacture and deploy.

If you are looking to get a free quote from one of the best IoT app development company in Canada, then please get in touch with us for any IoT Healthcare Solutions.

#Benefits of IoT in Healthcare #Best IoT app development company in Canada #Healthcare #Heart Attack Detection #IoT Healthcare Solutions #letsnurture #Role of IoT in Heart Attack Detection #Role of IoT in Heart Rate Monitoring

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technotale · 2 years ago

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"Unveiling Hidden Threats: How Narcotics Scanners Enhance Security Measures"

Here are some key features and applications of MEMS pressure sensors:

Read more @ https://techinforite.blogspot.com/2023/06/sensing-world-exploring-versatility-of.html

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alex121world · 5 years ago

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Embedded Processor Market – The Biggest Trends to Watch out for 2018-2026

These processors are also equipped with features such as storing and retrieving data from the memory.

Request for Report sample :https://www.trendsmarketresearch.com/report/sample/13342

Based on region, it is analyzed across North America, Europe, Asia-Pacific, and LAMEA along with their prominent countries. The key players profiled in the report include NXP Semiconductors, Broadcom Corporation, STMicroelectronics, Intel Corporation, Infineon Technologies AG, Analog Devices Inc., Renesas Electronics, Microchip Technology Inc., Texas Instruments, and ON Semiconductor.

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These key players have adopted strategies, such as product portfolio expansion, mergers & acquisitions, agreements, geographical expansion, and collaborations, to enhance their market penetration.

BY APPLICATION: • Consumer Electronics • Automotive • Industrial • Healthcare • Others

Make an Inquiry before Buying@:https://www.trendsmarketresearch.com/checkout/13342/Single

BY REGION

• North America o U.S. o Canada o Mexico

• Europe o UK o Germany o France o Russia o Rest of Europe

• Asia-Pacific o China o Japan o India o South Korea o Rest of Asia-Pacific

• LAMEA o Latin America o Middle East o Africa

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actutrends · 5 years ago

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Why TinyML is a giant opportunity

The world is about to get a whole lot smarter.

There are 250 billion microcontrollers in the world today. 28.1 billion units were sold in 2018 alone, and IC Insights forecasts annual shipment volume to grow to 38.2 billion by 2023.

Big potential

Early TinyML applications

It’s easy to talk about applications in the abstract, but let’s narrow our focus to specific applications likely to be available in the coming years that would impact the way we work or live:

Retail: By monitoring shelves in-store and sending immediate alerts as item quantities dwindle, TinyML can prevent items from becoming out of stock.

Before TinyML goes mainstream …

As intriguing as TinyML may be, we are very much in the early stages, and we need to see a number of trends occur before it gets mainstream adoption.

Other core ecosystem participants and tools will also be necessary:

Cloud players can invest in end-to-end optimized platform solutions that allow seamless exchange and processing of data between devices and the cloud.

Lifecycle TinyML tools need to be built that facilitate dataset management, algorithm development, and version management and that enhance the testing and deployment lifecycle.

In summary: TinyML is a giant opportunity that’s just beginning to emerge. Expect to see quite a bit of movement in this space over the next year or two.

[Find out about VentureBeat guest posts.]

TX Zhuo is General Partner at Fika Ventures. Huston Collins is Senior Associate at Fika Ventures.

The post Why TinyML is a giant opportunity appeared first on Actu Trends.

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transparencym-blog · 6 years ago

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Microcontrollers (MCU) Market to Expand with a CAGR of 9.0%

The report by Transparency Market Research (TMR), the global microcontrollers (MCU) market has highly competitive landscape. Some of the key players in the market are Fujitsu, Infineon Technologies, Freescale Semiconductor, Inc, Renesas Electronics Corporation, and Texas Instruments. The industry players are increasingly introducing improved and advanced microcontroller-enabled technology in various industry sectors for widening their customer base and to improve their product portfolio.

According to a report by Transparency Market Research (TMR), the global microcontrollers (MCU) market is expected to expand with a CAGR of 9.0% from 2012 to 2018, to attain the value of US$28.49 bn in 2018 from US$15.7 bn in 2011. The automotive industry accounted for the dominant revenue share of 31.4% in the market in 2012. Region-wise, Asia Pacific is dominating the global market followed by the EMEA. The cumulative share accounted by both region is nearly 70.5% of the global market in 2012

Extensive Applications in Automotive Industry to Drive Growth

The swift growth of global automotive industry is expected to impact positively on the growth of the microcontroller market. The availability of smartphones, phablets, tablets, and other touchscreens are boosting adoption of the MCUs which is likely to boost growth of the market. Additionally, MCUs have extensive application in consumer electronics such as microwave ovens, washing machines, and television. Furthermore, increasing demand for healthcare equipment such as portable glucometers and blood pressure monitors which uses microcontroller are also considerably supporting for the growth of market for MCUs.

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Awareness regarding smart energy management, along with the latest technological advancements, is key driver of the global microcontrollers (MCU) market. A favorable regulatory initiations are supporting deployment of smart grid systems which is likely to boost growth of the market. The technology is chiefly used in smart cards for ensuring better safety to electronic banking transactions and government IDs such as mass-transit fares, security applications, passports, and medical records.

Advent of Novel Features to Create Lucrative Opportunities

Despite of these growth prospects, malfunctioning of microcontroller in extreme climatic conditions such as extremely low and high temperatures are limiting growth of the market. Nevertheless, microcontrollers are manufactured as application-specific integrated circuits to be embedded inside devices they control. They comprise memory, peripherals, and processors coupled with the advent of the novel features such as GPS and keyless entry, the demand for MCUs in the automotive industry is higher than ever. This is creating lucrative opportunities for the growth in the global market for microcontroller.

#Microcontrollers (MCU) Market

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sakshitmr · 6 years ago

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Microcontrollers (MCU) Market is Expected to Expand at a CAGR of 46.1% from 2013 - 2019

Request Sample pages of premium Research Report: https://www.transparencymarketresearch.com/sample/sample.php?flag=B&rep_id=255

Extensive Applications in Automotive Industry to Drive Growth

Advent of Novel Features to Create Lucrative Opportunities

This information is comprised in the new report by TMR, titled “Microcontrollers (MCU) Market (Product - 8-bit, 16-bit, and 32-bit; Application - Automotive, Computer, Industrial, Consumer Goods, and Communication) - Global Industry Analysis, Size, Share, Growth and Forecast 2012 - 2018.”

View exclusive Global strategic Business report: https://www.transparencymarketresearch.com/microcontrollers-market.html

Global microcontrollers (MCU) market has been segmented as:

By Product:

8-bit

16-bit

32-bit

By Application:

Automotive

Computer

Industrial

Consumer Goods

Communication

By Geography:

North AmericaU.S.Canada

EuropeU.K.GermanyFranceSpainItalyRest of Europe

Asia PacificChinaJapanAustraliaIndiaRest of Asia Pacific

Latin AmericaBrazilMexicoRest of Latin America

Middle East & AfricaSouth AfricaSaudi ArabiaRest of Middle East & Africa

To understand geography trends, Download Sample Report at: https://www.transparencymarketresearch.com/sample/sample.php?flag=S&rep_id=255

About Us Transparency Market Research is a next-generation market intelligence provider, offering fact-based solutions to business leaders, consultants, and strategy professionals. Our reports are single-point solutions for businesses to grow, evolve, and mature. Our real-time data collection methods along with ability to track more than one million high growth niche products are aligned with your aims. The detailed and proprietary statistical models used by our analysts offer insights for making right decision in the shortest span of time. For organizations that require specific but comprehensive information we offer customized solutions through adhoc reports. These requests are delivered with the perfect combination of right sense of fact-oriented problem solving methodologies and leveraging existing data repositories. TMR believes that unison of solutions for clients-specific problems with right methodology of research is the key to help enterprises reach right decision. Contact Mr. Rohit Bhisey Transparency Market Research State Tower, 90 State Street, Suite 700, Albany NY – 12207 United States Tel: +1-518-618-1030 USA – Canada Toll Free: 866-552-3453 Email: [email protected] Website: http://www.transparencymarketresearch.com Research Blog: http://www.europlat.org/

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snehasahu-blog1 · 6 years ago

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Connected (Smart) Street Lights Market: Extensive Applications in Automotive Industry to Drive Growth

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