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A scientist from Russia has developed a new neural network architecture and tested its learning ability on the recognition of handwritten digits. The intelligence of the network was amplified by chaos, and the classification accuracy reached 96.3%. The network can be used in microcontrollers with a small amount of RAM and embedded in such household items as shoes or refrigerators, making them 'smart.' The study was published in Electronics.

Today, the search for new neural networks that can operate on microcontrollers with a small amount of random access memory (RAM) is of particular importance. For comparison, in ordinary modern computers, random access memory is calculated in gigabytes. Although microcontrollers possess significantly less processing power than laptops and smartphones, they are smaller and can be interfaced with household items. Smart doors, refrigerators, shoes, glasses, kettles and coffee makers create the foundation for so-called ambient intelligece. The term denotes an environment of interconnected smart devices. 

An example of ambient intelligence is a smart home. The devices with limited memory are not able to store a large number of keys for secure data transfer and arrays of neural network settings. It prevents the introduction of artificial intelligence into Internet of Things devices, as they lack the required computing power. However, artificial intelligence would allow smart devices to spend less time on analysis and decision-making, better understand a user and assist them in a friendly manner. Therefore, many new opportunities can arise in the creation of environmental intelligence, for example, in the field of health care.

Andrei Velichko from Petrozavodsk State University, Russia, has created a new neural network architecture that allows efficient use of small volumes of RAM and opens the opportunities for the introduction of low-power devices to the Internet of Things. The network, called LogNNet, is a feed-forward neural network in which the signals are directed exclusively from input to output. Its uses deterministic chaotic filters for the incoming signals. The system randomly mixes the input information, but at the same time extracts valuable data from the information that are invisible initially. A similar mechanism is used by reservoir neural networks. To generate chaos, a simple logistic mapping equation is applied, where the next value is calculated based on the previous one. The equation is commonly used in population biology and as an example of a simple equation for calculating a sequence of chaotic values. In this way, the simple equation stores an infinite set of random numbers calculated by the processor, and the network architecture uses them and consumes less RAM.

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The scientist tested his neural network on handwritten digit recognition from the MNIST database, which is considered the standard for training neural networks to recognize images. The database contains more than 70,000 handwritten digits. Sixty-thousand of these digits are intended for training the neural network, and another 10,000 for network testing. The more neurons and chaos in the network, the better it recognized images. The maximum accuracy achieved by the network is 96.3%, while the developed architecture uses no more than 29 KB of RAM. In addition, LogNNet demonstrated promising results using very small RAM sizes, in the range of 1-2kB. A miniature controller, Atmega328, can be embedded into a smart door or even a smart insole, has approximately the same amount of memory.

"Thanks to this development, new opportunities for the Internet of Things are opening up, as any device equipped with a low-power miniature controller can be powered with artificial intelligence. In this way, a path is opened for intelligent processing of information on peripheral devices without sending data to cloud services, and it improves the operation of, for example, a smart home. This is an important contribution to the development of IoT technologies, which are actively researched by the scientists of Petrozavodsk State University. In addition, the research outlines an alternative way to investigate the influence of chaos on artificial intelligence," said Andrei Velichko.

Originally posted HERE.

by Russian Science Foundation

Image Credit: Andrei Velichko

 

 

 

 

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Theoratical Embedded Linux requirements

Hardware

SoC

A System on Chip (SoC), is essentially an integrated circuit that takes a single platform and integrates an entire computer system onto it. It combines the power of the CPU with other components that it needs to perform and execute its functions. It is in charge of using the other hardware and running your software. The main advantage of SoC includes lower latency and power saving.

It is made of various building blocks:

  • Core + Caches + MMU – An SoC has a processor at its core which will define its functions. Normally, an SoC has multiple processor cores. For a “real” processor, e.g. ARM Cortex-A9. It’s the main thing kept in mind while choosing an SoC. Maybe co-adjuvanted by e.g. a SIMD co-processor like NEON.
  • Internal RAM – IRAM is composed of very high-speed SRAM located alongside the CPU. It acts similar to a CPU cache, and generally very small. It is used in the first phase of the boot sequence.
  • Peripherals – These can be a simple ADC, DSP, or a Graphical Processing Unit which is connected via some bus to the Core. A low power/real-time co-processor helps the main Core with real-time tasks or handle low power states. Examples of such IP cores are USB, PCI-E, SGX, etc.

External RAM

An SoC uses RAM to store temporary data during and after bootstrap. It is the memory an embedded system uses during regular operation.

Non-Volatile Memory

In an Embedded system or single-board computer, it is the SD card. In other cases, it can be a NAND, NOR, or SPI Data flash memory. It is the source of data the SoC reads and stores all the software components needed for the system to work.

External Peripherals

An SoC must have external interfaces for standard communication protocols such as USB, Ethernet, and HDMI. It also includes wireless technology protocols of Wi-Fi and Bluetooth.

Software

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First of all, we introduce the boot chain which is the series of actions that happens when an SoC is powered up.

Boot ROM: It is a piece of code stored in the ROM which is executed by the booting core when it is powered-on. This code contains instructions for the configuration of SoC to allow it to execute applications. The configurations performed by Boot ROM include initialization of the core’s register and stack pointer, enablement of caches and line buffers, programming of interrupt service routine, clock configuration.

Boot ROM also implements a Boot Assist Module (BAM) for downloading an application image from external memories using interfaces like Ethernet, SD/MMC, USB, CAN, UART, etc.

1st stage bootloader

In the first-stage bootloader performs the following

  • Setup the memory segments and stack used by the bootloader code
  • Reset the disk system
  • Display a string “Loading OS…”
  • Find the 2nd stage boot loader in the FAT directory
  • Read the 2nd stage boot loader image into memory at 1000:0000
  • Transfer control to the second-stage bootloader

It copies the Boot ROM into the SoC’s internal RAM. Must be tiny enough to fit that memory usually well under 100kB. It initializes the External RAM and the SoC’s external memory interface, as well as other peripherals that may be of interest (e.g. disable watchdog timers). Once done, it executes the next stage, depending on the context, which could be called MLO, SPL, or else.

2nd stage bootloader

This is the main bootloader and can be 10 times bigger than the 1st stage, it completes the initialization of the relevant peripherals.

  • Copy the boot sector to a local memory area
  • Find kernel image in the FAT directory
  • Read kernel image in memory at 2000:0000
  • Reset the disk system
  • Enable the A20 line
  • Setup interrupt descriptor table at 0000:0000
  • Setup the global descriptor table at 0000:0800
  • Load the descriptor tables into the CPU
  • Switch to protected mode
  • Clear the prefetch queue
  • Setup protected mode memory segments and stack for use by the kernel code
  • Transfer control to the kernel code using a long jump

Linux Kernel

The Linux kernel is the main component of a Linux OS and is the core interface between hardware and processes. It communicates between the hardware and processes, managing resources as efficiently as possible. The kernel performs following jobs

  • Memory management: Keep track of memory, how much is used to store what, and where
  • Process management: Determine which processes can use the processor, when, and for how long
  • Device drivers: Act as an interpreter between the hardware and the processes
  • System calls and security: Receive requests for the service from processes

To put the kernel in context, they can be interpreted as a Linux machine as having 3 layers:

  • The hardware: The physical machine—the base of the system, made up of memory (RAM) and the processor (CPU), as well as input/output (I/O) devices such as storage, networking, and graphics.
  • The Linux kernel: The core of the OS. It is a software residing in memory that tells the CPU what to do.
  • User processes: These are the running programs that the kernel manages. User processes are what collectively makeup user space. The kernel allows processes and servers to communicate with each other.

Init and rootfs – init is the first non-Kernel task to be run, and has PID 1. It initializes everything needed to use the system. In production embedded systems, it also starts the main application. In such systems, it is either BusyBox or a custom-crafted application.

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Whether you want to attempt initial entry into the IoT-sphere, or expand existing deployments, this book can help with your goals, providing deep understanding into all aspects of IoT.

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Edge Products Are Now Managed At The Cloud

Now more than ever, there are billions of edge products in the world. But without proper cloud computing, making the most of electronic devices that run on Linux or any other OS would not be possible.

And so, a question most people keep asking is which is the best Software-as-a-service platform that can effectively manage edge devices through cloud computing. Well, while edge device management may not be something, the fact that cloud computing space is not fully exploited means there is a lot to do in the cloud space.

Product remote management is especially necessary for the 21st century and beyond. Because of the increasing number of devices connected to the internet of things (IoT), a reliable SaaS platform should, therefore, help with maintaining software glitches from anywhere in the world. From smart homes, stereo speakers, cars, to personal computers, any product that is connected to the internet needs real-time protection from hacking threats such as unlawful access to business or personal data.

Data being the most vital asset is constantly at risk, especially if individuals using edge products do not connect to trusted, reliable, and secure edge device management platforms.

Bridges the Gap Between Complicated Software And End Users

Cloud computing is the new frontier through which SaaS platforms help manage edge devices in real-time. But something even more noteworthy is the increasing number of complicated software that now run edge devices at homes and in workplaces.

Edge device management, therefore, ensures everything runs smoothly. From fixing bugs, running debugging commands to real-time software patch deployment, cloud management of edge products bridges a gap between end-users and complicated software that is becoming the norm these days.

Even more importantly, going beyond physical firewall barriers is a major necessity in remote management of edge devices. A reliable Software-as-a-Service, therefore, ensures data encryption for edge devices is not only hackproof by also accessed by the right people. Moreover, deployment of secure routers and access tools are especially critical in cloud computing when managing edge devices. And so, developers behind successful SaaS platforms do conduct regular security checks over the cloud, design and implement solutions for edge products.

Reliable IT Infrastructure Is Necessary

Software-as-a-service platforms that manage edge devices focus on having a reliable IT infrastructure and centralized systems through which they can conduct cloud computing. It is all about remotely managing edge devices with the help of an IT infrastructure that eliminates challenges such as connectivity latency.

Originally posted here

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Industrial IoT Revolution

Why the Nvidia Jetson Nano is responsible for the biggest industrial IoT revolution these days

 
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It feels like yesterday when the Raspberry Pi foundation released the first-in-line Single Board Computer (SBC) to the market. Back in 2012, Raspberry Pi wasn't alone in the SBC growing market, however, it was the first to make a community-based product that brings the hardware and the software eco-system to a beautiful harmony on the internet. Before those days, embedded Linux based SBC's and SOM's were a place for Linux kernel and embedded hardware experts, no easy-to-use tools, ready Linux based distros, or most importantly without the enormous amount of questions and answers across the internet on anything related.

Today, 8 years later, the "2012 revolution" happens again

This time, it took a year to understand the impact of the new 'kid' in the market, but now, there are a few indications that defiantly build the route to a revolution.

The Raspberry Pi was the first to make embedded Linux easy while keeping the advantages of reliability and flexibility in terms of fitting to different kinds of industries applications. It's almost impossible to ignore the variety of industries where Raspberry Pi is in its hurt of products to save time-to-market and costs. The power of this magical board leans on the software side: The Raspberry Pi foundation and their community, worked hard across the years to improve and share their knowledge, but, at the same time, without notice or targeting, they brought the Pi development to an extremely "serverless" level.

The Nvidia Jetson Nano

Let's stop talking about the Raspberry Pi and focus on today's industry needs to understand better why the new kid in the town is here to change the market of IoT and smart products forever.

 
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 Why do we need to thanks Nvidia and the Jetson Nano?
 

The market is going forward. AI, Robotics, amazing-looking screen app Gui's, image processing, and long data calculations are all become the new standard of smart edge products.

If a few years ago, you would only want to connect your product to the cloud and receive anything valuable, today, product managers and developers compete in a much tougher industry era. This time, the Raspberry Pi can't be the technology hero again, its resources are limited and the eco-system starts to squint to a better-fit solution.

 
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NVIDIA Jetson devices in Upswift.io device management platform

The Jetson Nano is the first SBC to understood the necessary combination that will drive new products to use it. It's the first SBC designed in the mind of industrial powerful use cases, while not forgetting the prototyping stage and the harmony that gave the Raspberry Pi their success. It's the first solution to bring the whole package for developers and for hardware engineers with a "SaaS" feel: The OS is already perfect thanks to Ubuntu, there is plenty of software instructions by Nvidia and open-source ready-to-use tools custom made for the Jetson family, and for the hardware engineers: they are free to go with the System On Module (SOM) that is connected to a carrier board which includes all the necessary outputs and inputs to make the development stage even faster.

The Jetson Nano combination is basically providing the first world infrastructure for producing a "2020" product with complex software while working in a minimal budget and time-to-market. The Jetson Nano enables developers and product managers to imagine further without compromises, bringing tough software missions to the edge easily.

Originally posted here

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Industrial Prototyping for IoT

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ADLINK is a global leader in edge computing driving data-to-decision applications across industries. The company recently introduced I-Pi SMARC for Industrial IoT prototyping.

-       AdLInk I-Pi SMARC consists of a simple carrier paired with a SMARC Computer on Module

-       SMARC Modules are available from entry level PX30 Rockchip to top of the line Intel Apollo Lake.

-       SMARC modules are specifically designed for typical industrial embedded applications that require long life, high MTBF and strict revision control.

-       Use popular off the shelve sensors and create prototypes or proof of concepts on short notice.

Additional information can be found here

 

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Helium Expands to Europe

Helium, the company behind one of the world’s first peer-to-peer wireless networks, is announcing the introduction of Helium Tabs, its first branded IoT tracking device that runs on The People’s Network. In addition, after launching its network in 1,000 cities in North America within one year, the company is expanding to Europe to address growing market demand with Helium Hotspots shipping to the region starting July 2020. 

Since its launch in June 2019, Helium quickly grew its footprint with Hotspots covering more than 700,000 square miles across North America. Helium is now expanding to Europe to allow for seamless use of connected devices across borders. Powered by entrepreneurs looking to own a piece of the people-powered network, Helium’s open-source blockchain technology incentivizes individuals to deploy Hotspots and earn Helium (HNT), a new cryptocurrency, for simultaneously building the network and enabling IoT devices to send data to the Internet. When connected with other nearby Hotspots, this acts as the backbone of the network. 

“We’re excited to launch Helium Tabs at a time where we’ve seen incredible growth of The People’s Network across North America,” said Amir Haleem, Helium’s CEO and co-founder. “We could not have accomplished what we have done, in such a short amount of time, without the support of our partners and our incredible community. We look forward to launching The People’s Network in Europe and eventually bringing Helium Tabs and other third-party IoT devices to consumers there.”  

Introducing Helium Tabs that Run on The People’s Network
Unlike other tracking devices,Tabs uses LongFi technology, which combines the LoRaWAN wireless protocol with the Helium blockchain, and provides network coverage up to 10 miles away from a single Hotspot. This is a game-changer compared to WiFi and Bluetooth enabled tracking devices which only work up to 100 feet from a network source. What’s more, due to Helium’s unique blockchain-based rewards system, Hotspot owners will be rewarded with Helium (HNT) each time a Tab connects to its network. 

In addition to its increased growth with partners and customers, Helium has also seen accelerated expansion of its Helium Patrons program, which was introduced in late 2019. All three combined have helped to strengthen its network. 

Patrons are entrepreneurial customers who purchase 15 or more Hotspots to help blanket their cities with coverage and enable customers, who use the network. In return, they receive discounts, priority shipping, network tools, and Helium support. Currently, the program has more than 70 Patrons throughout North America and is expanding to Europe. 

Key brands that use the Helium Network include: 

  • Nestle, ReadyRefresh, a beverage delivery service company
  • Agulus, an agricultural tech company
  • Conserv, a collections-focused environmental monitoring platform

Helium Tabs will initially be available to existing Hotspot owners for $49. The Helium Hotspot is now available for purchase online in Europe for €450.

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I recently joined the Embedded Online Conference thinking I was going to gain new insights on embedded and IoT techniques. But I was pleasantly surprised to see a huge variety of sessions with a focus on modern software development practices. It is becoming more and more important to gain familiarity with a more modern software approach, even when you’re programming a constrained microcontroller or an FPGA.

Historically, there has been a large separation between application developers and those writing code for constrained embedded devices. But, things are now changing. The embedded world intersecting with the world of IoT, data science, and ML, and the deeper co-operation between software and hardware communities is driving innovation. The Embedded Online Conference, artfully organised by Jacob Beningo, represented exactly this cross-section, projecting light on some of the most interesting areas in the embedded world - machine learning on microcontrollers, using test-driven development to reduce bugs and programming an FPGA in Python are all things that a few years ago, had little to do with the IoT and embedded industry.

This blog is the first part of a series discussing these new and exciting changes in the embedded industry. In this article, we will focus on machine learning techniques for low-power and cost-sensitive IoT and embedded Arm-based devices.

Think like a machine learning developer

Considered for many year's an academic dead end of limited practical use, machine learning has gained a lot of renewed traction in recent years and it has now become one of the most interesting trends in the IoT space. TinyML is the buzzword of the moment. And this was a hot topic at the Embedded Online Conference. However, for embedded developers, this buzzword can sometimes add an element of uncertainty.

The thought of developing IoT applications with the addition of machine learning can seem quite daunting. During Pete Warden’s session about the past, present and future of embedded ML, he described the embedded and machine learning worlds to be very fragmented; there are so many hardware variants, RTOS’s, toolchains and sensors meaning the ability to compile and run a simple ‘hello world’ program can take developers a long time. In the new world of machine learning, there’s a constant churn of new models, which often use different types of mathematical operations. Plus, exporting ML models to a development board or other targets is often more difficult than it should be.

Despite some of these challenges, change is coming. Machine learning on constrained IoT and embedded devices is being made easier by new development platforms, models that work out-of-the-box with these platforms, plus the expertise and increased resources from organisations like Arm and communities like tinyML. Here are a few must-watch talks to help in your embedded ML development: 

  • New to the tinyML space is Edge Impulse, a start-up that provides a solution for collecting device data, building a model based around it and deploying it to make sense of the data directly on the device. CTO at Edge Impulse, Jan Jongboom talks about how to use a traditional signal processing pipeline to detect anomalies with a machine learning model to detect different gestures. All of this has now been made even easier by the announced collaboration with Arduino, which simplifies even further the journey to train a neural network and deploy it on your device.
  • Arm recently announced new machine learning IP that not only has the capabilities to deliver a huge uplift in performance for low-power ML applications, but will also help solve many issues developers are facing today in terms of fragmented toolchains. The new Cortex-M55 processor and Ethos-U55 microNPU will be supported by a unified development flow for DSP and ML workloads, integrating optimizations for machine learning frameworks. Watch this talk to learn how to get started writing optimized code for these new processors.
  • An early adopter implementing object detection with ML on a Cortex-M is the OpenMV camera - a low-cost module for machine vision algorithms. During the conference, embedded software engineer, Lorenzo Rizzello walks you through how to get started with ML models and deploying them to the OpenMV camera to detect objects and the environment around the device.

Putting these machine learning technologies in the hands of embedded developers opens up new opportunities. I’m excited to see and hear what will come of all this amazing work and how it will improve development standards and transform embedded devices of the future.

If you missed the conference and would like to catch the talks mentioned above*, visit www.embeddedonlineconference.com

*This blog only features a small collection of all the amazing speakers and talks delivered at the Conference!

Part 2 of my review can be viewed by clicking here

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Drivers are the backbone of the trucking industry and driver retention is one of the biggest concerns. Are you facing issues like driver shortage or driver retention? Advanced technology offers various solutions to transportation industry for such issues. Real-time monitoring, time-saving in shifts, better communication of drivers with managers, etc. simplify the work of drivers. Job dissatisfaction and operational inefficiencies are the major reasons why drivers leave the job. But this can be reduced, let’s see how.

How to improve the job satisfaction of drivers?
Trucking industry relies on the drivers for navigating through complex routes to transport sensitive goods safely and on time. It is a hectic job already and hence it is necessary that the drivers are satisfied with their job. Or else they will leave the job which hampers the business profits. What efforts can be taken for retention of drivers?

Simplifying the Driver’s Work using Technology
Drivers are constantly on the road. Traffic, bad weather conditions, etc. can be very irritating to them. However, using GPS systems, such conditions can be tracked proactively. The drivers can be directed to other routes which are having less traffic or some shifts can be canceled if the routes show bad weathers ahead. The drivers will not be annoyed by waiting for long hours and instead can take rest if the shifts are canceled due to bad weathers.

Automation and digitalization save time and efforts of the drivers. There are mobile apps which keep track of the load on the trucks. It saves the drivers of manual checking of load and also sends messages to the owners about the load. Also, in case of any theft or adding illegal loads to the truck, the owners can get instant messages right on their smartphones.

One such eminent example is the mobile app- Appweigh. It uses Bluetooth-enabled weight sensor to keep the track of the load on your truck. It is a budget-friendly app which combines the sensor and Bluetooth technology. Throughout the shipment of the trucks, the sensors detect the pressure on the tyres and clearly display the weight through AppWeigh on the smartphone of owners or fleet managers. The drivers don’t need to keep manual watch on the weight when the load reaches a certain destination. It is automatically sent to the owners.

Using IoT in Transportation for Better Communication and Time Management
Open communication with the drivers not only ensures transparency but also makes the drivers feel like true partners in the business. It encourages and engages them. Internet of things or IoT in transportation industry is playing a crucial role in connecting technology with people for more accurate results. It connects tools like sensors, RFID systems, GPS systems, smartphones, etc. to each other to gather vital data and communicate it to drivers and owners. Using this data, they can make informed decisions for improving various processes in fleet management. With such transparency, manual errors by drivers can be avoided and small issues can be discussed proactively before they turn into bigger problems.

Technology saves the drivers from keeping manual records of loads, timings, etc. as everything is automatically recorded. It reduces the stress of the drivers and ensures loyalty to the owners.

Making the Driver Health a Priority
Drivers’ health is the most critical topic when it comes to driver retention. A trucking industry can offer health benefits like health insurance plans, nutrition programs, free health screenings, etc. to drivers. Such benefits are an investment in your drivers. Also, the incorporation of smart cameras can reduce the risks of accidents. When the drivers feel safe and cared for their lives, your company reputation improves. They themselves will ask other drivers to join your company.

Giving Performance Incentives and Engaging Drivers
When drivers are appreciated and rewarded for their good work, it inspires them to do better and also be stable with your trucking industry. Financial incentive systems can be used to reward the most productive and safest drivers. Technology can be used to evaluate the drivers’ performance fairly. Real-time coaching and user-friendly solutions to any issues will help the drivers to progress faster and feel supported. The drivers who work hard to improve their performance can be awarded with the performance incentives. This will also encourage and engage fellow drivers. 

 

Conclusion
Smart technologies are providing highly efficient solutions to transportation industry. Along with driver retention, these technologies help in real-time visibility of the processes, maintaining the vehicle health, improving warehouse and yard management, etc. which enormously boost the business profits. IoT in transportation industry provides robust security services to drivers as well as the freight. Reliable data that owners get from smart technical solutions lets them take the right decisions to maintain their workforce. It enhances driver satisfaction and retention rates.

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