<h2 dir="ltr" id="isPasted">Introduction</h2>Industrial machinery, from robots to crane booms, require sensing and guidance to ensure their effective use. Beyond the industrial world, the latest video cameras detect and compensate for movement and vibration to create high-quality imagery. In fact, accurate sensing of orientation and movement is integral to everything from communication satellites to farming equipment.Inertial measurement units (IMUs) allow the precise measurement of motion and orientation. When combined with accurate positioning information provided by global navigation satellite systems (GNSS), IMUs can provide location and velocity information, even when satellite signals are weak or obstructed.Selecting the right IMU should be easy. Manufacturer’s data sheets provide access to all the information an engineer needs to choose the perfect component for their application. And yet, engineers are designing inaccuracy into their equipment as they are unable to gain a full understanding of how IMUs perform from data sheets alone.In this paper, we will highlight three key challenges that engineers face when specifying an IMU for their next design. We will help them to read between the lines of the data sheet to understand the information they need. We will look at how manufacturers test their products, and we will explain how the environment can affect the performance of IMUs.<iframe width="540" height="370" src="https://c8056756.sibforms.com/serve/MUIFAFzOYRmakTTHP2FX2FyHP3QxXkV_prDOkSjxVq9r73O2ZYgeTBdxzjryZ1uiV-lwmna05gucMW0bsczCM_L1y82QMtCTmxYMAjh47rKxZQYNnUMylX2XNlQ7TpTNyLxACx2rLDFoQXvwdl20h6UYtI005O05w77j-WIyUR8ngfSBpL3p7bGQ8Cu-7pjUFhSkY3xZfKxVGSlQ" frameborder="0" scrolling="auto" allowfullscreen="" style="display: block;margin-left: auto;margin-right: auto;max-width: 100%;" class="fr-draggable"></iframe> <h2 dir="ltr">Chapter 1: Getting the Whole Picture</h2>Engineers selecting the components for a new design will rely upon the manufacturer's information. The wealth of information provided by data sheets and catalogues allows the engineer to make decisions based on the component’s predicted performance. The key attributes that must be understood when selecting IMUs are bias instability and angle random walk (ARW). Bias instability is caused by tiny fluctuations in an IMU’s zero-bias level. This is the reading that the sensor delivers when it is at rest, and therefore provides the baseline for an IMU that is stationary. IMUs are affected by a range of external conditions, including variations in temperature and vibration, which reduce the accuracy and stability of the IMU's output. This change is characterized by a slow, long-term drift and is measured in degrees of drift per second (deg/s).In contrast to bias instability, angle random walk is caused by random high-frequency noise inside the IMU which causes variations in the measurement of angular velocity over time. The errors that ARW tries to quantify grow with the square root of time, and so the accumulated angle error will grow over a long period.Both attributes are, therefore, heavily influenced by time, a problem that is made worse by the dynamic conditions that IMUs are intended to measure. However, the data that is typically provided in IMU data sheets does not reflect this dynamic nature, and both bias instability and ARW are often published as a single number. This reduction of a complex system to a single number does not provide the engineer with a clear prediction of its performance over time. By relying on single attributes, engineers run the risk of designing inaccuracies into their device before it is even used.Instead of relying on the single value of the gyro bias instability and ARW, engineers can turn to Allan deviation plots. An Allan deviation plot (or Allan variance plot) is a graphical tool that provides a more useful understanding of the stability and noise characteristics of IMUs. The plot is unique to each IMU and helps identify the effects of different types of noise by showing how the variance of a sensor’s output changes over different averaging times. This is especially useful for assessing the drift and noise behaviour over time.This time dependency is the key advantage of using Allan deviation instead of traditional standard deviation. Standard deviation is a useful tool if the mean of the data is expected to be constant, and the measurements do not drift over time. This is not the case with IMUs. An Allan plot is derived from data collected from the IMU over a period of time, calculated at an interval known as tau (𝜏). This represents the averaging time, and the higher the tau, the more that individual extreme readings will be averaged out.The image below shows a typical Allan deviation plot for an IMU, in which the low point corresponds to the bias instability, whereas the value corresponding to tau of 1 sec (1 second averaging) represents the angular random walk, reflecting the higher frequency fluctuations that define ARW.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1733673242419-banner+epson.jpg" style="width: 100%px;" class="fr-fic fr-dib">Another data point concerns warm-up time. Extreme temperatures can affect the warm-up time of the IMU. This is the period after activation that the IMU needs to reach its operational state. In just one example, after hours exposed to sub-zero temperatures, the warm-up time for an IMU may be considerably longer than published to allow the internal components to stabilize before they can provide accurate readings.To avoid designing undesired inaccuracy into a new device, it is therefore vital for the engineers to access the best possible data. The preferred path for the engineer is to work with an IMU manufacturer that can provide this comprehensive information, for the alternative is for the engineer to conduct their own evaluation, a time-consuming process.While the information presented in data sheets is important, the engineer will only gain a real insight into the performance of their chosen component by understanding the underlying data and how it will affect their application in the long run.<h2 dir="ltr">Chapter 2: IMUs in the Real World</h2>Accurate and comprehensive information is therefore vital, but it is important for the engineer to understand the conditions under which it was obtained. Systems and equipment that use IMUs are regularly deployed beyond the safe environment of the laboratory into the real world. The conditions they encounter can have a significant effect on the performance of IMUs.An example of these tough conditions can be found in modern farming. The agricultural industry is embracing the latest sensing technology, collecting data to maximize efficiency. &nbsp;This data is used to make decisions, from identifying optimum soil conditions to deploying the right resources.&nbsp;Accurate positioning is essential for the smart farm. The ability to precisely control planting, fertilizing and harvesting activities allows the farmer to improve efficiency, yields, and sustainability. The huge growth in the development of autonomous tractors, harvesters, and drones places even more emphasis on navigation and positioning. These systems make use of global navigation satellite systems (GNSS) to allow these machines to operate within centimeters of their intended paths, without the need for human intervention.However, GNSS signals may not always be available as satellite signals can become disrupted or blocked. Atmospheric conditions, interference from structures and geographical features, along with the orbital geometry of the satellites themselves can all cause loss of signals. Without satellite guidance, autonomous machinery cannot obtain the accurate position that is so important to precision farming.Under these circumstances, the IMU becomes the principal means by which the equipment can locate their position. Using a process of dead reckoning, the IMU can measure the movement from the last known position provided by GNSS until the signal can be reacquired. The same method can be employed by a huge range of industries far from the smart farm, from the delivery of medical supplies by drone into remote locations to the automated loading of ships at ports.This reliance on IMUs in the real world means that they must withstand a range of conditions. Some of the most important effects to consider are those caused by shock and vibration. IMUs can be affected by both high and low frequencies. Some frequencies can cause an effect called sensor saturation in which the IMU can no longer work correctly as the vibration exceeds the IMU’s limiting value. Engineers must also be aware of the physical damage that vibration and sudden shock can cause to equipment.Engineers must also understand the effect of extreme temperatures on IMUs. In the earlier example, we looked at the need for accurate positioning on the smart farm. The agricultural environment is one of the harshest in the world, and vehicles are designed to function in an extraordinary range of conditions. Whether deployed to the frozen tundra in the Arctic circle or the unforgiving heat of deserts, equipment is exposed to ambient temperatures from -40°C to near the boiling point of water.This range of temperatures has a significant impact on the accuracy and performance of IMUs. Bias instability changes with temperature, which can lead to cumulative errors, especially for measurements based on integration over time. To complicate matters further, the drift in bias caused by changes in temperature are often nonlinear. This means that the designer needs to calibrate their system over the expected operating temperature range to ensure accuracy.Engineers must therefore understand the environment into which their equipment will be deployed. Identifying harsh conditions on the road or in the factory will allow engineer to choose the correct IMU based on accurate data. This may require information obtained from the IMU manufacture that is not contained in the data sheet. It is essential to understand how temperature will affect warm-up time, and to request a calibration profile that details temperature-dependent drifts.<h2 dir="ltr">Chapter 3: Consistency is the Key</h2>While it is important for the engineer to understand the conditions to which their design will be exposed, it is also crucial to know how the data provided by the manufacturer was created. This has important implications for consistency. Data sheets for IMUs often present typical performance values, without providing clear information about how this information was obtained. In many cases, the published information is an average, but the data sheet will rarely provide details about the statistical methodology used to obtain the average. Without the all-important description of this typical performance, the engineer can only guess how the IMU will perform in their specific application.When evaluating IMUs, it is important to understand the level of statistical confidence behind the performance data. Where some manufacturers will publish performance at a 1-sigma level, others will use 2-sigma. The statistical difference between different sigma levels has important implications for high-performance applications in which consistency is key.Sigma levels are use used in statistics to describe a confidence level in the data provided. In a normal distribution of data, a 1-sigma (1σ) level represents one standard deviation away from the mean. At this level, roughly two-thirds (in fact, 68.27%) of the data values will fall within this one standard deviation from the mean. In real terms, it means that there is a two-thirds probability that any single measurement will fall within this range.However, if a manufacturer bases their data on a 2-sigma (2σ) level, this probability rises to more than 95%. As a result, any one measurement will fall within the predicted range 19 times out of 20, delivering a far higher level of confidence in the data provided. In the context of IMUs, this confidence translates into an expectation of accuracy over time.This variation in predicted consistency is further complicated by the testing regime employed by the manufacturer. Some manufacturers adopt a strategy of batch testing, evaluating only a small number of units to provide the data required. Other manufacturers may conduct multiple tests on a single unit in the expectation that this will provide an accurate prediction of the performance of the entire range. The result is that the listed performance may not accurately reflect the variability between different IMUs from the same production batch. This variability can be significant, particularly for critical characteristics like bias instability.Unfortunately, the only way to truly understand how an IMU will perform in a real-world application is for the engineer to perform their own evaluation. While data sheets can provide a baseline, real-world testing is necessary to understand the variability between units and ensure consistent performance in the field. Working with a trusted manufacturer that publishes clear and comprehensive information can go a long way to helping the engineer with their understanding.<iframe width="540" height="370" src="https://c8056756.sibforms.com/serve/MUIFAFzOYRmakTTHP2FX2FyHP3QxXkV_prDOkSjxVq9r73O2ZYgeTBdxzjryZ1uiV-lwmna05gucMW0bsczCM_L1y82QMtCTmxYMAjh47rKxZQYNnUMylX2XNlQ7TpTNyLxACx2rLDFoQXvwdl20h6UYtI005O05w77j-WIyUR8ngfSBpL3p7bGQ8Cu-7pjUFhSkY3xZfKxVGSlQ" frameborder="0" scrolling="auto" allowfullscreen="" style="display: block;margin-left: auto;margin-right: auto;max-width: 100%;" class="fr-draggable"></iframe> <h2 dir="ltr">Conclusion</h2>Selecting the right inertial measurement unit is a complex task. Not only are IMUs required to accurately measure movement, but they are expected to deliver high reliability in some of the most demanding conditions..Engineers need to understand how these conditions will affect the performance of their IMU. The roles played by extremes of temperature and the physical demands of dynamic applications must be considered before the engineer can have confidence on their design. Unfortunately, some manufacturers do not provide easy access to the detailed information that engineers need. Therefore, when selecting an IMU, here are the key considerations for engineers:<ul><li dir="ltr">Remember that the data sheet might not contain the depth of information required. Do not rely on single-number attributes, and obtain Allan deviation plots to gain a full understanding of the device’s performance.</li><li dir="ltr">Device testing takes place in the controlled environment of the laboratory, but the real-world is not so accommodating. Extremes of temperature and physical demands play a huge role in the reliability and accuracy of IMUs. Engineers should ensure that they understand their environment.</li><li dir="ltr">Take the time to understand how IMUs are tested. Confidence in performance comes from extensive testing methods, and clearly published data from the manufacturer. If necessary, engineers should be ready to conduct their own evaluation.</li></ul><a href="https://global.epson.com/products_and_drivers/sensing_system/imu/">Epson</a> manufactures a comprehensive range of IMUs for a huge array of applications, from earthquake detection to the precise positioning of satellites in space. Epson creates highly capable sensing systems that offer outstanding precision, high stability, and low noise to deliver confidence to design engineers in the most demanding industries.

Understanding IMU data sheets, manufacturers' testing methods, and the impact of environmental factors on performance.

Three Key Considerations for Selecting IMUs

In the fast-paced world of electronics and innovation, having access to the right tools is essential for designers and engineers looking to bring their concepts to life quickly and efficiently. Development kits are a key resource in this process, offering pre-built solutions that allow for faster prototyping and testing of new ideas. Mouser Electronics, a leading global distributor, is renowned for providing one of the most extensive ranges of off-the-shelf development kits, ensuring that designers can access the tools they need when they need them.In this article, Mouser’s Director of Technical Content for EMEA, Mark Patrick, shares his expert insights by presenting his ‘Top 5 Picks’ from the latest development kits available. These kits have been carefully selected based on their innovative features, ease of use, and the potential they offer to accelerate the design process. Whether you're working on AI applications, IoT solutions, or embedded systems, these development kits can give your project the jumpstart it needs.<h2>Long-range connectivity</h2>Digi XBee® XR 868&nbsp;This development kit includes the Digi XBee 868 RF MHz module with a U.FL antenna connector, antennas, and a development board. It supports long-range connectivity applications in Europe, operating between 863MHz and 870MHz. The module facilitates point-to-point and mesh networking with a line-of-sight range exceeding 14 kilometers, ideal for the agriculture and energy sectors. The device employs intelligent interference management using Listen Before Talk and Adaptive Frequency Agility, reducing interference by scanning frequencies and shifting channels seamlessly.<a href="https://www.mouser.com/new/embedded-solutions/engineering-tools/digi-xbee-xr-868-kit/n-8mwysZ1npagf">XBee® XR 868 Development Kit - Digi | Mouser</a><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1729582453944-digi.jpg" style="width: 100%px;" class="fr-fic fr-dib"><h2>Curiouser and curiouser</h2>Microchip WBZ450 EV22L65AMicrochip’s WBZ450 Curiosity board is an efficient, modular development platform that supports rapid prototyping and demonstrates the features, capabilities, and interfaces of Microchip’s WBZ450PE BLUETOOTH® Low Energy and Zigbee® RF module. The EV22L65A board offers integrated programming/debugging features using the PICkit™ Onboard 4 (PKOB4) debugger interface. A Micro-USB cable is required to power up and program the board.<a href="https://www.mouser.com/new/embedded-solutions/engineering-tools/microchip-ev22l65a-board/n-8mwysZ2puw22">WBZ450 Curiosity Board (EV22L65A) - Microchip Technology | Mouser</a><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1729582723571-micro.jpg" style="width: 100%px;" class="fr-fic fr-dib"><h2>Arm-based MCU&nbsp;</h2>STMicro STM32H7S78-DK Discovery kitSTMicroelectronics’ STM32H7S78-DK Discovery kit demonstrates and develops applications for the Arm® Cortex®-M7 core-based STM32H7S7L8H6H microcontroller. The kit comes with various hardware features, including USB Type-C™, Octo-SPI flash memory, Hexadeca-SPI PSRAM devices, audio codec, digital microphones, ADC, user button, and flexible extension connectors. These connectors facilitate easy wireless connectivity, analog applications, and sensor expansion.<a href="https://www.mouser.com/new/embedded-solutions/engineering-tools/stm-stm32h7s78-dk-kit/n-8mwysZ2cjsva">STM32H7S78-DK Discovery Kit - STMicro | Mouser</a><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1729582777405-st.jpg" style="width: 100%px;" class="fr-fic fr-dib"><h2>Rugged and reliable embedded wireless</h2>Advantech WLNNA&nbsp;Advantech’s WLNNA Series evaluation kit is a platform for evaluating, testing and developing Airborne Enterprise Device Server Modules. The Advantech WLNNA kit offers size, cost, power consumption, and performance advantages over other wireless solutions. Ideal for rugged and reliable applications needing an embedded IEEE 802.11a/b/g/n compliant wireless engine, the kit includes a WLNNA Series Evaluation Board with connectors and headers providing access to all module interfaces.<a href="https://www.mouser.com/new/embedded-solutions/engineering-tools/advantech-wlnna-kit/n-8mwysZ27k4mu">WLNNA Series Evaluation Kit - Advantech | Mouser</a><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1729582820389-brief+2.png" style="width: 100%px;" class="fr-fic fr-dib"><h2>IoT control</h2>M5Stack M5NanoC6 boardThis board features an ESP32-C6FH4 MCU supporting Wi-Fi 6, Zigbee®, and an infrared emitter for controlling infrared IoT devices. The device's ceramic antenna ensures stable wireless communication. Programmable RGB beads enable personalized visual effects. The Grove interface allows flexible expansion of various M5 devices, which are customizable for different communication protocols.<a href="https://www.mouser.com/new/embedded-solutions/engineering-tools/m5stack-m5nanoc6-board/n-8mwysZ2sl8dv" id="isPasted">M5NanoC6 Board - M5Stack | Mouser</a><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1729582849615-m5.jpg" style="width: 100%px;" class="fr-fic fr-dib">

In this article,  Mouser’s Director, Technical Content, EMEA, Mark Patrick, presents his ‘Top 5 Pick’ of recently-released dev kits.

The development tools you need

The latest large language models (LLMs), like the newly released ChatGPT-4o, are truly astonishing. They are multimodal, meaning they have multiple senses and can interpret text, images, and audio information and respond in a seemingly human-like manner.But how do they relate to edge AI, and what role does Edge Impulse play in this? This question comes up frequently in a range of conversations.Today we are sharing an exciting new Edge Impulse functionality that shows just one of the powerful ways that LLMs can be utilized on the edge: using GPT-4o to train a 2,000,000x smaller model, that runs directly on device.<iframe width="640" height="360" src="https://www.youtube.com/embed/Jou0aRgGiis?&amp;wmode=opaque&amp;rel=0&amp;enablejsapi=1&amp;origin=https://www.wevolver.com" frameborder="0" allowfullscreen="" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true">&nbsp;&nbsp;</iframe><h3 id="isPasted">The challenge of LLMs for real-time insights</h3>Let's say a business has a camera pointed at their factory floor. A manager can ask an LLM, "Is there a person not wearing a hard hat standing close to the machine?" The LLM is capable of analyzing the frame and answering yes or no; that response can then be used to trigger actions according to predetermined safety protocols.This is incredibly powerful because 1. the model understands what it sees, and 2. you can use natural language to ask questions about what is happening. This approach is called zero-shot learning, where you don't need to train a model beforehand.However, there are downsides to deploying zero-shot models in real environments. State-of-the-art LLM models have hundreds of billions of parameters, making them huge and slow. They typically run in the cloud, leading to latency issues. Even fast models like ChatGPT-4o take more than a second to analyze a single image, which is too slow for many real-time applications.<a href="https://form.jotform.com/242533284499365"><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1728891006740-5+Rising+Trends+for+AI+Adoption+in+Manufacturing+%283%29.png" style="width: 100%px;" class="fr-fic fr-dib"></a><h3>One solution: Labeling data with LLMs and Edge Impulse</h3>To address these issues, we need similar capabilities that can run closer to or even on the edge. Imagine the same factory floor but instead with a simple camera where everything runs locally. This setup offers low latency and low cost, without the need for cloud services.Using this type of system locally with LLMs isn't feasible, given the massive size of LLM models. However, with Edge Impulse, one can use an LLM's understanding to train a much smaller model. This process involves a form of transferring knowledge from a large model to a smaller one.Edge Impulse specializes in bringing AI to the edge, with over 300,000 projects created to build AI applications that run self-contained on devices from Arm Cortex-M (via <a href="https://www.edgeimpulse.com/blog/announcing-arm-keil-integration/">open-CMSIS</a>, reaching over 10,000+ devices) to <a href="https://edgeimpulse.com/nvidia/nvidia-tao">Nvidia</a>-based GPUs and beyond.The new Edge Impulse LLM functionality allows a user to bring LLM-based AI to the edge to automatically analyze and label visual data, including video uploads, and apply a specific understanding of what's in frame without needing someone to manually input any labels. Using the LLM's intelligence on just a specific request allows Edge Impulse to train a vision-based model from it that is compact enough to run on-device. It's a powerful and exciting application.<table><tbody><tr><td><h3>New whitepaper: Discover the 5 AI Trends Transforming Manufacturing</h3><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1728891039779-1728891039779.png" class="fr-fic fr-dii">The whitepaper, "5 Rising Trends for AI Adoption in Manufacturing," offers key insights into how artificial intelligence is transforming manufacturing processes. It explores survey data from over 150 industry leaders, revealing that optimizing production and improving product quality are top goals driving AI adoption. The report also discusses the rise of AI solutions for worker safety, real-time insights to boost profitability, and the growing importance of internal expertise in implementing AI. Download the full whitepaper to gain a deeper understanding of these trends and how AI/ML is shaping the future of manufacturing.<a href="https://form.jotform.com/242533284499365" target="_blank" rel="nofollow" class="button-main-action">DOWNLOAD YOUR FREE COPY</a></td></tr></tbody></table><h3>Project walkthrough</h3>Let's build a project to see how this works. In the above video I walked through my house, capturing a video of my living room filled with my kids' toys. Using ChatGPT, I can ask questions about this video, such as whether a children's toy is in view. Here's how we can create a smaller model to perform this same task, on-device:<ol><li>Data Collection: I recorded a video of my living room.</li><li>Data Processing: I uploaded the video to Edge Impulse. The data was initially unlabeled.</li><li>Labeling with GPT-4o: Using the new transformation block "Label image data using GPT-4o," I asked GPT-4o to label the images. The block discards any blurry or uncertain images, providing a clean dataset.</li><li>Model Training: I split the video into images, resulting in about 500 labeled items. I used <a href="https://edgeimpulse.com/nvidia/nvidia-tao" rel="noreferrer">NVIDIA TAO</a> to train a small model with these images. The model had only 800,000 parameters, significantly smaller than the original LLM.</li><li>Deployment: I deployed the model, running at 10 frames per second on an Arduino Nicla Vision. The model accurately detected toys on-device, in real-time, without requiring cloud services.</li></ol><h3>Results</h3>The small model performed exceptionally well, identifying toys in various scenes quickly and accurately. By distilling knowledge from the large LLM, we created a specialized, efficient model suitable for edge deployment. The smaller model ran at 50 frames per second in a browser on my iPhone and even on a microcontroller with minimal accuracy loss.<hr><h3 id="isPasted">Download the whitepaper: 5 AI Trends Transforming Manufacturing</h3><div data-message-author-role="assistant" data-message-id="a9ce89dd-4eb9-44c7-9d28-e3c7d10f7094" dir="auto" data-message-model-slug="gpt-4o"><div data-message-author-role="assistant" data-message-id="1c2c7603-add5-4aa4-84c9-81aa2c1371a2" dir="auto" data-message-model-slug="gpt-4o">Stay ahead of the curve on how AI is transforming businesses with the whitepaper "5 Rising Trends for AI Adoption in Manufacturing." The guide offers valuable insights to help leaders implement AI strategies that optimize production, improve product quality, and drive innovation. Download your free copy now:<iframe id="JotFormIFrame-242533284499365" title="Edge Impulse Form" allowtransparency="true" src="https://form.jotform.com/242533284499365" frameborder="0" style="min-width:100%;max-width:100%;height:900px;border:none;" scrolling="no" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"> </iframe></div></div>

The latest large language models (LLMs), like the newly released ChatGPT-4o, are truly astonishing. They are multimodal, meaning they have multiple senses and can interpret text, images, and audio information and respond in a seemingly human-like manner.

Bringing Large Language Models to the Edge with GPT-4o and NVIDIA TAO

Arduino micro-controller on the left, Raspberry Pi in the middle and C.H.I.P. single-board computer on the right

This article provides a comprehensive analysis of Arduino vs Raspberry Pi, highlighting their essentials, key features, and recent developments, helping you make an informed decision for your next project. 

Arduino vs Raspberry Pi: Choosing the Right Platform for Your Next Project

This article will guide you through the key features, functions, and configurations of the ESP8266 Pinout, setting the foundation for successful IoT implementations.

ESP8266 Pinout: Unlocking the Power of IoT's Favorite Microcontroller

I3C is becoming a strong alternative to I2C. Learn about the main differences, benefits, and uses of these two important communication protocols. Explore the world of inter-integrated circuit communication and see which protocol is best for your next project.

I3C vs I2C: Unraveling the Battle of Communication Protocols

Espressif ESP32 series microcontroller module

This guide serves as a roadmap for engineers to navigate the intricacies of the ESP32 pinout, understand its significance, and explore its applications in modern engineering projects. 

ESP32 Pinout: A Comprehensive Guide for Engineers

This article was written by Adam Taylor for Mouser Electronics<h2 id="isPasted">Introduction</h2>In this project, we will configure the AMD Kria™ KR260 Robotics Starter Kit to control a Trossen Robotics ReactorX 150 robot arm. This complex robot arm uses <a href="https://www.mouser.com/new/robotis/robotis-dynamixel-x-modules/" rel="nofollow" target="_blank" data-internal="false">ROBOTIS DYNAMIXEL</a> servos, which contain not only motors but also microcontrollers and networking capability. While this enables the complex motor drives needed for robotics applications, it also means that each servo has several different drive and control mechanisms. Typically, when working with servos and robotics applications of this complexity, significant processing is required to plan and address the robotic movement.To interact with and control such robots, engineers often use the <a href="https://www.ros.org/" rel="nofollow" target="_blank" data-internal="false">Robot Operating System (ROS)</a>, which runs on Linux. The most used version of ROS is ROS 2, which updated the ROS frameworks and tools to work with a wider variety of environments, provide support for real-time environments, and use significantly updated APIs. ROS 2 provides developers with functionalities such as hardware drivers, robot models, datatypes, and support for perception and simultaneous localization and mapping (SLAM). ROS 2 also provides a series of tools that aid in the development or operation of systems such as RViz, which provides a 3D visualization, and Gazebo, a simulator.ROS 2 is built around a graph architecture; within this architecture, processing takes place in nodes that can receive and post data about the node, such as sensor, control, planning, actuator positioning, or current state. Nodes are connected on the ROS graph by topics, which are communication pipelines to which nodes can publish data and receive information. Along with nodes and topics, a node may also advertise services. The services have a single result, such as capturing a frame of video, sampling a sensor, or opening an actuator.<h2>Bill of Materials</h2><ul><li><a href="https://www.mouser.com/new/xilinx/xilinx-kria-kr260-kit/" rel="nofollow" target="_blank" data-internal="false">AMD Kria™ KR260 Robotics Starter Kit</a></li><li>Monitor with DisplayPort™ compatibility</li><li>USB keyboard and mouse</li><li><a href="https://www.trossenrobotics.com/reactorx-150-robot-arm.aspx" rel="nofollow" target="_blank" data-internal="false">ROBOTIS ReactorX In150</a> robotic arm</li></ul><h2>Resources</h2><ul><li><a href="https://ubuntu.com/certified/202202-29985" rel="nofollow" target="_blank" data-internal="false">Ubuntu Linux distribution image for the AMD Kria™ KR260 Robotics Starter Kit</a> (Ubuntu)</li><li><a href="https://www.xilinx.com/products/som/kria/kr260-robotics-starter-kit/kr260-getting-started/setting-up-the-sd-card-image.html" rel="nofollow" target="_blank" data-internal="false">Getting Started with the AMD Kria KR260 Robotics Starter Kit</a> (AMD)</li><li><a href="https://sourceforge.net/projects/vcxsrv/files/vcxsrv/1.20.14.0/" rel="nofollow" target="_blank" data-internal="false">VcXsrv Windows X Server Files</a></li></ul><h2>Software Setup</h2>In this project, we will install the Trossen Robotics packages and ROS 2 on the AMD Kria™ KR260 Robotics Starter Kit, which will enable you to control the robot arm.<h3>Installing Ubuntu</h3>Start by downloading and installing the Ubuntu Linux distribution image for the AMD Kria™ KR260 kit, available in the Resources section. Once the image is available, flash the image to an SD card following the instructions provided by AMD in Getting Started with Kria KR260 Robotics Starter Kit in the Resources section.Once you have successfully booted the AMD Kria KR260 kit, you will need to update the Ubuntu installation to ensure we can install ROS 2 correctly.<ol><li>Install resolvconf.</li></ol><code>&nbsp; &nbsp;sudo apt update</code><code>&nbsp; &nbsp;sudo apt install resolvconf</code><ol start="2"><li>With resolvconf installed, ensure it is running.</li></ol><code>&nbsp; &nbsp;sudo systemctl status resolvconf.service</code><ol start="3"><li>Once you have confirmed the service is running, add your preferred DNS server. In this example, we use Googles DNS.</li></ol><code>&nbsp; &nbsp;echo "nameserver 8.8.8.8" | sudo tee -a /etc/resolvconf/resolv.conf.d/head</code><code>&nbsp; &nbsp;echo "nameserver 8.8.4.4" | sudo tee -a /etc/resolvconf/resolv.conf.d/head</code><ol start="4"><li>Because we made changes, we need to restart the services.</li></ol><code>&nbsp; &nbsp;sudo systemctl restart resolvconf.service</code><code>&nbsp; &nbsp;sudo systemctl restart systemd-resolved.service</code><ol start="5"><li>Prevent Ubuntu on the KR260 from going to sleep.</li></ol><code>&nbsp; &nbsp;sudo gsettings set org.gnome.desktop.session idle-delay 0</code><code>&nbsp; &nbsp;sudo systemctl mask suspend.target</code><h3>Installing ROS 2</h3>To install ROS 2 and the packages to control the Trossen Robotics X Series arm, we will modify the instructions slightly to those used for the Raspberry Pi, which, like the AMD Kria™ KR260 kit, uses the Arm64 (aarch64) instruction set architecture.The latest version of ROS 2 supported by Trossen’s libraries is ROS 2 Humble. Use the following commands to install ROS 2 and the Trossen libraries:<code>cd ~</code><code>sudo apt install curl</code><code>curl 'https://raw.githubusercontent.com/Interbotix/interbotix_ros_manipulators/main/interbotix_ros_xsarms/install/rpi4/xsarm_rpi4_install.sh' &gt; xsarm_rpi4_install.sh</code><code>sed -i 's/sudo apt-get update &amp;&amp; sudo apt -y upgrade/sudo apt-get update/g' xsarm_rpi4_install.sh</code><code>chmod +x xsarm_rpi4_install.sh</code><code>./xsarm_rpi4_install.sh -d humble -j rx150</code><h2>Demo Application</h2>With ROS 2 installed on the AMD Kria™ KR260 kit, we will run one of the provided demo applications. Open two terminal windows on the KR260.<ol><li>In the first terminal window, enter the following command:</li></ol><code>&nbsp; &nbsp;ros2 launch interbotix_xsarm_control xsarm_control.launch.py robot_model:=rx150</code><ol start="2"><li>In the second terminal, enter the following command:</li></ol><code>&nbsp; &nbsp;python3 /home/ubuntu/interbotix_ws/src/interbotix_ros_manipulators/interbotix_ros_xsarms/interbotix_xsarm_control/demos/python_ros2_api/bartender.py</code>This will run an application that demonstrates the control of the robot arm.<h3>Demo Breakdown</h3>By examining the code in this application, we can see how we can control the arm using the Python programming language. First, we need to import the InterbotixManipulatorXS arm package from the InterbotiX libraries we installed. To do this, enter the following command: <code>from interbotix_xs_modules.arm import InterbotixManipulatorXS</code>With these packages installed, we can start creating applications that manipulate the arm using a Python application.Looking at the example code, we can see the first thing we do is initialize it with the parameters of the robot, the model, type, and its end effector.With the robot (shown as <code>bot</code> in the code) defined, the application first checks the robot has the correct number of joints for the demo. Once it has been confirmed as being suitable for the demo, the robot is controlled by using several Python commands that set the pose, or single positions, as required. The gripper can also be opened and closed using Python calls.<code>bot = InterbotixManipulatorXS("rx150", "arm", "gripper")</code><code>&nbsp; &nbsp; if (bot.arm.group_info.num_joints &lt; 5):</code><code>&nbsp; &nbsp; &nbsp; &nbsp; print('This demo requires the robot to have at least 5 joints!')</code><code>&nbsp; &nbsp; &nbsp; &nbsp; sys.exit()</code><code>&nbsp; &nbsp; bot.arm.set_ee_pose_components(x=0.3, z=0.2)</code><code>&nbsp; &nbsp; bot.arm.set_single_joint_position("waist", np.pi/2.0)</code><code>&nbsp; &nbsp; bot.gripper.open()</code><code>&nbsp; &nbsp; bot.arm.set_ee_cartesian_trajectory(x=0.1, z=-0.16)</code><code>&nbsp; &nbsp; bot.gripper.close()</code>Finally, the demo ends by safely positioning the robot to its home position.<code>&nbsp; &nbsp; bot.arm.go_to_home_pose()</code><code>&nbsp; &nbsp; bot.arm.go_to_sleep_pose()</code>Knowing this, we can start creating own custom applications, as shown in <a href="https://youtu.be/g4xX4aowwI8" rel="nofollow" target="_blank" data-internal="false">this video</a>. But first, we need to be able to remotely develop applications on the AMD Kria™ KR260 board.<h2>Remote Development</h2>To develop our remote application, we will use Visual Studio Code on the development machine. The Python application we create using Visual Studio Code on our development machine will be in the file system of the AMD Kria™ KR260 kit. This also has the benefit of requiring no connected display, keyboard, or mouse, as we can access everything from the development environment.We also want to be able to remotely view the visualization of the robot arm as we develop our application (in simulation mode or actual execution).To enable remote development and visualization, we must establish SSH connection and enable X11 forwarding.<ol><li>Use Windows Powershell to install OpenSSH using the following commands.</li></ol><code>&nbsp; &nbsp;Get-WindowsCapability -Online | Where-Object Name -like 'OpenSSH*'</code><code>&nbsp; &nbsp;# Install the OpenSSH Client</code><code>&nbsp; &nbsp;Add-WindowsCapability -Online -Name OpenSSH.Client~~~~0.0.1.0</code><code>&nbsp; &nbsp;# Install the OpenSSH Server</code><code>&nbsp; &nbsp;Add-WindowsCapability -Online -Name OpenSSH.Server~~~~0.0.1.0</code><code>&nbsp; &nbsp;# Start the sshd service</code><code>&nbsp; &nbsp;Start-Service sshd</code><code>&nbsp; &nbsp;# OPTIONAL but recommended:/code&gt;</code><code>&nbsp; &nbsp;Set-Service -Name sshd -StartupType 'Automatic'</code><code>&nbsp; &nbsp;# Confirm the Firewall rule is configured. It should be created automatically by setup. Run the following to verify</code><code>&nbsp; &nbsp;if (!(Get-NetFirewallRule -Name "OpenSSH-Server-In-TCP" -ErrorAction SilentlyContinue | Select-Object Name, Enabled)) {</code><code>&nbsp; &nbsp; &nbsp; &nbsp; Write-Output "Firewall Rule 'OpenSSH-Server-In-TCP' does not exist, creating it..."</code><code>&nbsp; &nbsp; &nbsp; &nbsp; New-NetFirewallRule -Name 'OpenSSH-Server-In-TCP' -DisplayName 'OpenSSH Server (sshd)' -Enabled True -Direction Inbound -Protocol TCP -Action Allow -LocalPort 22</code><code>&nbsp; &nbsp;} else {</code><code>&nbsp; &nbsp; &nbsp; &nbsp; Write-Output "Firewall rule 'OpenSSH-Server-In-TCP' has been created and exists."</code><code>&nbsp; &nbsp;}</code><ol start="2"><li>With OpenSSH installed, create a key using the following command.</li></ol><code>&nbsp; &nbsp;ssh-keygen</code>By default, the system will save the keys to <code>C:\Users\&lt;user&gt;/.ssh/id_rsa.</code><ol start="3"><li>Exchange keys using the following command.</li></ol><code>&nbsp; &nbsp;type $env:USERPROFILE\.ssh\id_rsa.pub | ssh ubuntu@&lt;board_ip&gt; "cat &gt;&gt; .ssh/authorized_keys"</code>To establish a SSH connection from Visual Studio Code, we must first install the remote SSH option from the extensions (Figure 1).<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzE3MTQ3NTU5NTE3LUFNRHBfZmlnMS53ZWJwIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 100%px;" class="fr-fic fr-dib">Figure 1: SSH remote connection application. (Source: Mouser Electronics)Once this is installed, we can connect to the AMD Kria KR260 board (Figure 2), which will allow us to develop our applications remotely in the KR260 file system.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzE3MTQ3NTc0MDA4LUFNRHBfZmlnMi53ZWJwIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 100%px;" class="fr-fic fr-dib">Figure 2: Establishing an SSH remote connection. (Source: Mouser Electronics)To be able to use X11 forwarding, we must first install cXsrv Windows X Server (available in the Resources section) on our development machine. Run X Server and configure it. For screenshots fo this setup see <a href="https://resources.mouser.com/robotics/mastering-multi-axis-robotics-guide" target="_blank" rel="noopener noreferrer">here</a>. &nbsp;Connect to the display by running the following command in a command window on the development machine:<code>set DISPLAY=127.0.0.1:0.0</code>We can then initiate an SSH connection with the AMD Kria KR260 board and start developing our robot application.<code>ssh -Y &lt;user&gt;@&lt;board_ip&gt;</code>As this runs, the robot arm will move and you will see a visualization of this movement on X Server (Figure 7).<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzE3MTQ3Njc4MjM0LUFNRHBfZmlnNy53ZWJwIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 100%px;" class="fr-fic fr-dib">Figure 7: X Server robotic arm visualization (Source: Mouser Electronics)<h2 id="isPasted">Wrap Up</h2>Robotics can be complicated. In this project, we have seen that the AMD Kria™ KR260 kit can enable rapid development of robotic solutions using ROS 2. The KR260 provides the ability to accelerate features within the programmable logic element or to integrate further system control elements such as time-sensitive networking within the programmable logic.<hr>AMD, and the AMD Arrow logo, Kria, and combinations thereof are trademarks of Advanced Micro Devices, Inc. Other product names used herein are for identification purposes and may be trademarks of their respective owners.

In this project, we will configure the AMD Kria™ KR260 Robotics Starter Kit to control a Trossen Robotics ReactorX 150 robot arm. This complex robot arm uses ROBOTIS DYNAMIXEL servos, which contain not only motors but also microcontrollers and networking capability.

Mastering Multi-Axis Robotics: A Step-by-Step Guide

Pose detection technology leverages advanced machine learning algorithms to interpret human movements in real-time, enabling seamless, intuitive device control through simple gestures.

Enhancing Smart Homes with Pose Detection Using Neuromorphic Computing and Edge AI

Noise suppression includes both emission and immunity suppression, and various measures are in place in robots to prevent malfunction due to immunity.

Robot Emission Suppression Measures

This article provides a detailed understanding of microprocessor vs integrated circuit, their basics, key differences, future trends, challenges, and how they synergize to form the backbone of our digital systems.

Microprocessor vs Integrated Circuit: Unveiling the Core of Modern Technology

<h2 dir="ltr" id="isPasted">Introduction</h2>Embedded systems are integral to our daily lives, found in everything from home appliances to industrial machines. The heart of any such system is its microcontroller (MCU). This tiny yet important component is more than just a piece of hardware; it is the decision-maker that drives the functionality, efficiency, and success of the entire project. Choosing the right MCU is not a mere technical decision; it's a strategic one that significantly impacts the overall system performance, power consumption, and functionality of your embedded design. A well-chosen MCU ensures that your system runs reliably and efficiently, handling tasks with the necessary speed and consuming power with careful consideration. Conversely, a poorly selected MCU can lead to underperformance, high energy consumption, and increased costs, potentially compromising the project's success. Therefore, understanding the main benefits of MCUs and aligning them with your project's needs is an important part of your project in the field of embedded systems engineering.<h2>Understanding Project Requirements&nbsp;</h2>Selecting the right MCU for an embedded project is an important decision relying on the project's specific requirements, as the chosen MCU directly influences the functionality, efficiency, and scalability of the project. This process begins with a thorough analysis of the project's goals and technical needs. Firstly, it’s important to analyze the project's functional requirements, which includes understanding the tasks the MCU needs to perform, such as data processing, signal control, or communication with other devices.&nbsp;The processing power of the MCU determines how quickly and efficiently it can execute tasks, important for applications that require real-time processing or handling complex algorithms. It is essential to assess the processing needs of your project, whether it involves simple control tasks or more demanding computational work. A more powerful MCU might be necessary for intensive tasks, but it could also mean higher costs and energy consumption.Memory is another critical factor. The MCU's memory is split into two main types: flash (for storing your program) and RAM (for temporary data storage during operation). The size of your code and the data it needs to handle will dictate the memory requirements. Insufficient memory can lead to performance issues or limit the scope of your project, so it’s important to estimate memory needs accurately.Furthermore, the nature and number of I/O interfaces determine how the MCU will interact with other components like sensors, actuators, and user interfaces. Count the number and type of I/O pins you need and ensure that the MCU can accommodate them. Additionally, consider the need for specialized functions, such as analog-to-digital converters (ADCs) or pulse-width modulation (PWM) outputs.But also, many embedded projects require MCUs to communicate with other devices. This could be through standard protocols like UART, SPI, or I2C for onboard communication, or more advanced interfaces like Ethernet or Wi-Fi for network connectivity. The choice of MCU should align with these communication needs, ensuring it supports the required protocols without necessitating extensive additional hardware.<h2>Microcontroller Architectures</h2>In order to choose the right MCU it is important to understand the various MCU architectures, like AVR, ARM, and PIC, each having unique properties and influence differently like performance, power efficiency, and development flexibility.Firstly, AVR architecture, widely recognized through the Arduino platform, stands out for its user-friendly approach. Ideal for beginners and educational purposes, AVR chips are known for their simplicity in programming and debugging. They offer a decent balance of power efficiency and processing capability, making them well-suited for moderate-level applications like hobbyist projects or basic automation systems.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzA5ODE0NDk4OTIwLVBST0dSQU0gUk9NLnBuZyIsImVkaXRzIjp7InJlc2l6ZSI6eyJ3aWR0aCI6OTUwLCJmaXQiOiJjb3ZlciJ9fX0=" style="width: 100%px;" class="fr-fic fr-dib">ARM architecture, on the other hand, is known for its high-performance capabilities and remarkable energy efficiency. ARM-based MCUs are versatile, scaling from lower-end Cortex-M series to the more powerful Cortex-R and Cortex-A series, making them applicable for a broad range of applications, from simple devices to complex, compute-intensive applications like smart home systems, advanced robotics, and IoT devices.PIC architecture is favored for its adaptability and low power consumption, comes in a variety of options, accommodating a wide range of needs from basic to complex. PIC chips are particularly noted for their robustness and reliability, making them a preferred choice in industrial applications and consumer electronics.Selecting the appropriate architecture requires a careful evaluation of your project's needs. If your project demands high processing power and energy efficiency, ARM-based MCUs are typically the go-to choose. For projects that prioritize ease of use and cost-effectiveness, especially for those new to embedded systems, AVR might be more appropriate. Meanwhile, for applications that require a balance between performance, power efficiency, and versatility, PIC MCUs are often a reliable choice.<h2 dir="ltr">Power Consumption Considerations</h2>The energy efficiency of a MCU can greatly influence the longevity and reliability of a system, affecting everything from user experience to maintenance costs. In battery-operated devices, such as portable medical devices, wearables, or remote sensors, minimizing power consumption is important. Especially when high power usage leads to frequent battery replacements or recharges, which can be impractical or costly. Additionally, in applications where it is difficult to access the device for maintenance, such as embedded sensors in smart infrastructure, efficient power usage becomes even more important for ensuring long-term operation.Modern MCUs are equipped with various low-power modes and energy-saving features. These include sleep or idle modes, where the MCU slows down or turns off certain functions when they are not needed. For instance, a MCU might power down its CPU while keeping its memory active, allowing it to quickly resume full operation when required. Other features include efficient clock management and power gating, which selectively shuts down unneeded peripherals.Achieving the right balance between processing power and energy efficiency is a challenging aspect of MCU selection. Higher processing power generally leads to higher power consumption, but this is not always a linear relationship. Advanced MCUs are designed to provide optimum performance with minimal energy use. When selecting a MCU, consider the processing demands of your application and compare them against the power specifications of the MCU. Often, it’s possible to find a MCU that offers sufficient processing capabilities while still maintaining a low energy footprint.<h2 dir="ltr">Development Ecosystem and Tools</h2>The choice of a MCU is greatly influenced by the availability and quality of its development ecosystem. This ecosystem encompasses the software tools, libraries, community support, and documentation available for a particular MCU. A strong development ecosystem simplifies every stage of the project lifecycle, from prototyping to deployment. It provides developers with vital resources like compilers, integrated development environments (IDEs), and code libraries. Additionally, a community support network can be an important benefit, offering solutions to common problems, advice on best practices, and shared experiences from fellow developers.A well-supported ecosystem can significantly streamline the development process. Access to comprehensive libraries and frameworks allows developers to avoid misleading steps for common tasks and functionalities. Moreover, good documentation and active community forums can speed up the troubleshooting process, reducing development time.Development tools are important especially in simplifying debugging and testing. Advanced IDEs offer features like code completion, error highlighting, and sophisticated debugging tools that can detect and help resolve issues quickly. These tools are invaluable in ensuring the reliability and performance of the final product. Moreover, some ecosystems provide simulation tools allowing developers to test their code in a virtual environment before deploying it on actual hardware, further easing the development process.<h2>Costs</h2>The price of a microcontroller can significantly impact the overall budget of a project, especially in large-scale productions or cost-sensitive applications. The cost of a microcontroller is not limited to its purchase price alone. It encompasses the long-term expenses associated with development, maintenance, and potential scalability needs. In projects where large quantities of MCUs are required, even a small difference in unit cost can substantially affect the total expenditure. Therefore, it is important to choose wisely and select a MCU that fits within the budget while meeting the project requirements.<h2>Practical Examples&nbsp;</h2>In this section, two practical examples will be shortly observed. In the first one, application of MCUs for home automation systems is of interest. This automation project has requirements to have low power consumption, Wi-Fi connectivity, and the ability to handle multiple sensor inputs. An ESP32 microcontroller was chosen for its built-in Wi-Fi capabilities, efficient power management, and ample GPIO pins for sensor connectivity. The result was a cost-effective, energy-efficient system capable of seamless integration into a smart home environment.Another example is installation of MCUs as an industrial monitoring device, where a robust MCU for monitoring equipment is required. The critical factors were reliability under harsh conditions, extended temperature range, and real-time processing. A microcontroller from the STM32 family was selected for its ruggedness, wide temperature tolerance, and high processing power. This choice ensured reliable and accurate monitoring under industrial conditions.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzA5Nzk0ODM2OTA1LW1vdXNlciBtY3UuanBnIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 100%px;" class="fr-fic fr-dib"><h2 dir="ltr" id="isPasted">Conclusion&nbsp;</h2>The selection of the right microcontroller extends far beyond technical specifications, it also involves a thoughtful and customized approach that aligns with the specific needs and constraints of the project. A microcontroller is not just a component; it is the driving force behind the functionality, efficiency, and scalability of an embedded system. The diverse range of architectures, from AVR to ARM to PIC, offers a spectrum of options catering to different project needs. By understanding these various elements and applying this knowledge carefully, engineers and developers can make informed decisions that lead to efficient, reliable, and successful embedded systems.

Selecting the right microcontroller (MCU) is crucial in embedded systems engineering, as it significantly influences the system's performance, power consumption, and overall success.

Choosing the Right Microcontroller for Your Embedded Project

Wearable electronic devices designed to be worn while in use are becoming more popular and powerful as they can monitor various aspects of human health, activity, and environment. However, these devices also face challenges such as limited battery life, data privacy, and network bandwidth. To overcome these challenges, edge computing and AI/ML are emerging as key technologies that can enable wearable devices to process data locally, reduce latency, and enhance user experience.&nbsp;As wearable technologies evolve to meet market needs, new devices are upgrading features such as basic BLE connectivity by adding the ability to run AI algorithms on neural processors on-device rather than in the cloud. This reduces the amount of data that needs to be transmitted over the network, saving energy and bandwidth. With the AI/ML models running in silicon, wearable devices can, therefore, learn from data and perform tasks such as recognition, classification, prediction, and optimization without incurring additional latency, data costs, and power consumption. However, these improvements require greater processing power and capabilities from embedded microcontrollers (MCUs) and application processors. At the same time, consumers expect longer battery life from their wearables. Thus, new chips must balance connectivity and higher processing power with low power consumption while maintaining a small enough package size.&nbsp;This case study explores the challenges for the next generations of wearable MCUs with a specific look at <a href="https://alifsemi.com/" target="_blank" rel="noopener noreferrer">Alif Semiconductor's®</a> Balletto™ family of BLE-enabled MCUs for wearable applications.<h2 dir="ltr">The current landscape</h2>The wearables market, which includes common accessories such as smart bands, smart rings, smartwatches, and medical devices, such as glucose monitoring devices is projected to grow to USD 118.16 billion by 2028 at a 13.8% CAGR. Demand for multimedia devices, smartphones, fitness trackers, and health wearables drives growth. Increasing health awareness fuels interest in fitness tracking devices for activities like cycling and running, contributing to market expansion.[2,3]Wearables have evolved significantly in recent years, presenting enhanced capabilities in various domains, such as AI processing and wirelessly transmitting data. New market demands have driven the expansion of wearable features. For example, the COVID-19 pandemic made blood oxygen sensing a must-have feature for many smartwatches. As a result, Apple and Samsung included blood oxygen monitoring in their smartwatches in 2020.Wearables incorporate edge AI-based solutions to process huge amounts of data in real-time. AI-enabled microcontrollers are a must to carry out these large AI/ML workloads efficiently and quickly. The integration of artificial intelligence enables new features, such as:<ul><li dir="ltr">Tracking and analyzing changes in users’ health data.</li><li dir="ltr">Sending users alerts for noise levels or air quality.</li><li dir="ltr">Personalize your device based on your tracked activity.&nbsp;</li></ul>Equally important as collecting and processing user data is the ability to wirelessly share data between devices. As such, Bluetooth connectivity has risen as a top requirement for wearables including functionalities, such as:&nbsp;<ul><li dir="ltr">Sending an alert to emergency services if a fall is detected.</li><li dir="ltr">Sharing important health updates with an approved health care professional.</li><li dir="ltr">Sharing workout metrics with a friend to stay motivated.</li></ul><h2 dir="ltr">Bluetooth LE changes the status quo in wearables</h2>Among the many low-power wireless technologies on the market, Bluetooth Low Energy (BLE) technology is uniquely suited for wearable devices that require long battery life measured in days or even weeks before needing to be recharged. BLE allows wearables to easily connect and communicate with other devices, such as smartphones, tablets, exercise equipment, or even other wearables.&nbsp;This enhances the device's functionality by enabling it to share exercise data, receive timely notifications, and more. Increasingly, wearable devices are adding audio support for which BLE is uniquely positioned. With the addition of support for the Low Complexity Communication Codec (LC3), BLE is now capable of use cases such as broadcast audio and shared or group listening experiences with LE Audio’s Auracast feature. For example, a wearable device can use the on-device neural processor such as Arm’s Ethos-U55 to run a convolutional neural network (CNN) model for gesture recognition or activity classification based on the motion data captured by inertial measurement units (IMUs). The device can then use BLE to communicate with other devices or a smartphone app to provide feedback or instructions to the user.&nbsp;In the Smart Home, wearables can be integrated into a smart home system, with AI algorithms learning the user's habits and preferences to automate various home appliances. BLE 5.4's enhanced performance and range can facilitate efficient communication with many IoT devices in the home. For example, a wearable could learn that a user likes to listen to calming music when they get home from work and automatically instruct the smart speaker to play the user's favorite playlist when they walk through the door.&nbsp;Additionally, features allow wearables to be located precisely, while location-based services (LBS) such as personalized fitness coaching (created by learning from a user’s previous workout data) can be enabled. It can suggest workout routines tailored to the user's fitness goals and current health status. The wearable device can recognize the user’s activity and posture using an ML model that runs on the Ethos-U55 core and sends the data to a cloud service via Enhanced Attribute Protocol (EAP). This could enable personalized, reliable, and secure fitness tracking for the user.&nbsp;<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzEzMTY0MTY0MDQ1LVdob29wLUJvZHlUcmFpbmluZy1Ub3AuanBnIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 100%px;" class="fr-fic fr-dib">Next-gen wearables will enable highly-personal fitness tracking. Credit: WHOOP.&nbsp;AI/ML algorithms can be trained to detect patterns in biometric data gathered by wearables, such as heart rate, blood pressure, and oxygen saturation levels. BLE's enhanced broadcast communication mechanism (EAD, PAwR) can allow these devices to send real-time health risk alerts to other devices, such as a smartphone, even in congested or high-interference environments. For example, a wearable could alert a user or a healthcare provider if it detects signs of an impending health crisis like a heart attack or stroke.&nbsp;When you combine AI-enabled MCUs with BLE, wearables become more personalized, efficient, secure, and useful in our everyday lives. Alif Semiconductor <a href="https://alifsemi.com/press-release/first-ble-matter-wireless-microcontroller-with-neural-co-processor-for-ai-ml/" target="_blank">has just announced</a> their Balletto family of BLE and Matter microcontrollers&nbsp;with neural processing for AI/ML workloads, which seeks to exceed the standard for low-power BLE operations.<h2 dir="ltr">Hardware accelerated machine learning</h2>To meet the market-need for state-of-the-art wearables, Alif Semiconductor offers AI/ML-enabled microcontrollers with dedicated NPU accelerators that allow heavy AI/ML workloads to run without compromising battery life. <a href="https://alifsemi.com/products/balletto/" target="_blank" id="isPasted">The Alif Balletto family</a> of microcontrollers introduces the world's most power-efficient microcontroller with on-device secure AI/ML processing, top-notch security, and ample peripherals for applications requiring integrated Bluetooth Low Energy and 802.15.4-based connectivity.&nbsp;The Balletto family boasts the powerful Arm® Cortex®-M55 core, equipped with double-precision FPU and Helium Vector Extensions, operating at a remarkable speed of up to 160MHz. Arm Ethos-U55 is a dedicated NPU accelerator that offloads AI/ML operations from the Cortex-M55 processor, allowing the M55 cores to operate in very low-power modes. This is especially important for wearables like hearing aids, which need to have a long battery life. Equally as important for wearables is having a small form-factor design, which Balletto can provide in a sub 16mm ² package. &nbsp;&nbsp;Balletto’s Ethos™-U55 Neural Processing Unit (NPU) can improve the performance and efficiency of speech recognition machine learning models by up to two orders of magnitude, excluding the 5-10x improvement already provided by the Cortex-M55 core over previous generation Cortex-M CPUs. This is ideal for wearables that rely on voice commands, improving processing speed and lowering latency.&nbsp;<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzEyODQyNDU1ODU5LWFsaWYgbWFpbiAyLnBuZyIsImVkaXRzIjp7InJlc2l6ZSI6eyJ3aWR0aCI6OTUwLCJmaXQiOiJjb3ZlciJ9fX0=" style="width: 100%px;" class="fr-fic fr-dib">Balletto and Ensemble families. Image credit: Alif Semiconductor.<h3 dir="ltr" id="isPasted">Battery-friendly microcontrollers powered by Alif’s aiPM™ technology&nbsp;</h3>The Balletto family also tackles a common problem seen by previously existing AI/ML-powered microcontrollers: short battery life. Connected wearables or mission-critical devices like hearing aids depend on long battery life for users. Their ability to function is hindered when there is a need for heavy local processing and AI/ML. Balletto MCUs are the most power-efficient in their class, thanks to Alif's innovative aiPM technology. This technology manages all aspects of the device, including individual processing cores for real-time control, neural processing, DSP and audio, deep security, and radio protocols capable of BLE 5.4 with LE Audio, Thread, and Matter. Balletto devices also include a wide range of interfaces and large memories, all monolithically integrated inside extremely small packages. This makes them ideal for demanding battery-powered applications such as wearables and hearables.Alif Semiconductor’s aiPM technology manages the power usage of the device through 8 power domains. It activates only the necessary chip sections and deactivates them when not in use, resulting in a highly efficient always-on region when there is a high requirement for local processing and AI/ML. The integrated dual-PA and on-chip antenna gives customers control of the reliability-range equation by providing tuning options of 4 dBm or up to 10 dBm Bluetooth transmit power. &nbsp;Balletto’s Bluetooth radio has a receive current of 1.5mA and a transmit current of 2.0mA, making it power conscious.Additionally, off-die peripherals produce higher energy consumption for wearables, while Balletto’s on-die solution fosters maximum efficiency. Utilization of the internal bus fabric effectively lowers access time, dynamic power, and standby power, ultimately providing more granular and precise power control at a lower cost.&nbsp;<h3 dir="ltr">Balletto family multi-layered security</h3>In addition to handling processing requirements, microcontrollers for wearables must ensure data security. Alif Balletto microcontrollers provide exceptional security features with multiple layers of protection, including:<ul><li dir="ltr">Root of Trust</li><li dir="ltr">Unique device ID for each individual chip</li><li dir="ltr">Dedicated security processor</li><li dir="ltr">Dedicated protected memory</li><li dir="ltr">Configurable firewalls that regulate access of each CPU to sections of memories and individual peripherals and extend the capabilities of the standard Arm Trust Zone security partitioning</li></ul>Alif Semiconductor is the only company in this market segment that injects at manufacturing, a unique public key infrastructure (PKI) key pair to support immutable&nbsp;hardware root of trust (RoT) from secure boot to strong authentication and remote software attestation capabilities.As a result, the personal data acquired by sensors and processed by the Alif Balletto microcontrollers are secured against third-party attacks.Users can securely manage all aspects of the device lifecycle, including key management, certificate management, remote updates, and more. Legacy MCUs may stop at TrustZone, but Alif takes you beyond TrustZone to ensure that multi-core devices are protected by not allowing two CPUs to access the same Secure domain and share secrets.&nbsp;<h3 dir="ltr">Graphics and imaging</h3>The Balletto family includes a number of hardware features to help offload traditionally CPU-intensive workloads from the MCU/MPU cores to improve performance and keep current consumption in check. One such feature is an integrated 2D graphics accelerator that can help speed up drawing operations by 4-10x compared to an unaccelerated system. This, combined with a set of imaging and display interfaces, which include parallel as well as MIPI-CSI2 and MIPI-DSI interfaces for 24-bit RGB displays and 8 to 16-bit cameras, provide everything needed for responsive and buttery-smooth graphics on pixel-dense screens.<h2 dir="ltr">Conclusion</h2>As the wearable technology market continues to expand, the convergence of edge AI and BLE technology has enabled wearables to become smarter, more efficient, and more secure. Alif Semiconductor's Balletto family of MCUs represents a significant leap forward in meeting the ever-growing demands of the wearables market. These innovative solutions empower wearables to not only collect and process data but also to provide real-time, personalized, and secure experiences, all while maintaining long-lasting battery life and optimizing form factor. The future of wearables is here, and it's exciting.<h3 dir="ltr">References</h3><ol><li dir="ltr">GlobeNewswire. <a href="https://www.globenewswire.com/en/news-release/2023/03/15/2627702/0/en/2023-2028-Smart-Wearables-Market-Size-Share-and-Trends-Analysis-Report-By-Y-O-Y.html" target="_blank">2023- 2028 Smart Wearables Market Size, Share and Trends Analysis Report By Y-O-Y</a>.</li><li dir="ltr">businesswire. <a href="https://www.businesswire.com/news/home/20220104005806/en/Global-Wearable-Technology-Market-Trends-Analysis-Report-2021-2028-Adoption-of-Fitness-Trackers-and-Health-based-Wearables-is-Anticipated-to-Propel-Growth---ResearchAndMarkets.com" target="_blank">Global Wearable Technology Market Trends &amp; Analysis Report 2021-2028: Adoption of Fitness Trackers and Health-based Wearables is Anticipated to Propel Growth - ResearchAndMarkets.com.</a></li></ol><h3>Further Research</h3>Ramesh Nair, 2023. “<a href="https://www.safetymint.com/blog/smart-wearables/" target="_blank">Smart Wearables: Advancing Safety in the Workplace</a>”StartUs Insights, 2023. “<a href="https://www.startus-insights.com/innovators-guide/wearables-trends/" target="_blank">Top 10 Wearables Trends in 2023</a>”GlobalData, 2022. “<a href="https://www.medicaldevice-network.com/comment/wearable-technology-iot/" target="_blank">Wearable technology market continues to rise</a>”Lisa Eadicicco, 2022. “<a href="https://www.cnet.com/tech/mobile/smartwatches-have-measured-blood-oxygen-for-years-but-is-it-useful/" target="_blank">Smartwatches Have Measured Blood Oxygen for Years. But Is This Useful?</a>”Fi Forest, 2022. “<a href="https://www.pharmaceutical-technology.com/sponsored/wearables-managing-complexities-data-privacy-consent/" target="_blank">Wearables: managing complexities in data privacy and consent in healthcare tech</a>”Industry News, 2022. “<a href="https://www.helpnetsecurity.com/2022/11/16/alif-semiconductor-telit/" target="_blank">Alif Semiconductor partners with Telit to develop and deploy IoT edge devices</a>”Alif Semiconductor, 2023. “<a href="https://alifsemi.com/ensemble/" target="_blank">Supplying next generation scalable microcontrollers</a>”Arrow, 2022. “<a href="https://www.arrow.com/en/research-and-events/articles/alif-semiconductor" target="_blank">Alif Semiconductor</a>”Eduardo Montanez, 2022. “<a href="https://www.nxp.com/company/blog/nxp-i-mx-rt-mcu-technology-powers-our-smartwatch-future:BL-NXP-IMX-RT-MCU-TECHNOLOGY" target="_blank">NXP i.MX RT MCU Technology Powers Our Smartwatch Future</a>”

Learn how bluetooth connected AI/ML microcontrollers are setting the bar for next generation wearables.

Leading Wearables into a New Era with Cutting-Edge Connected Bluetooth LE based MCUs

Microcontrollers and microprocessors are fundamental components in electronics and computing. Both play crucial roles in operating various devices, from smartphones to embedded systems in automobiles and home appliances. This article studies their differences in architecture, performance, and more.

Microcontroller vs Microprocessor: A Comprehensive Guide to Their Differences and Applications

<h1 dir="ltr" id="isPasted">Introduction</h1>In the intricate tapestry of modern technology, where devices are becoming increasingly interconnected and intelligent, the ability to accurately interpret the world around us is paramount. This is where the concept of 'sensor fusion' shines brightly, helping to make sense of vast quantities of data. Sensor fusion, in its essence, is the art and science of amalgamating data from multiple sensors to provide a more holistic, accurate, and reliable view of the environment. It's akin to piecing together a jigsaw puzzle, where each sensor provides a fragment of the bigger picture, and when fused together, these fragments reveal a comprehensive image.As we navigate through this article, we will delve deep into the technical intricacies and methodologies underpinning sensor fusion. We'll explore its applications, from autonomous vehicles to healthcare, and highlight the challenges that developers and engineers face in this domain. Furthermore, we'll introduce the transformative potential of integrating sensor fusion with cutting-edge tools like the SensiEDGE CommonSense addon for Sony Spresense and the paradigm of Edge AI. By the end, readers will gain a comprehensive understanding of the current landscape of sensor fusion and the promising capability it brings.<iframe width="540" height="600" src="https://c8056756.sibforms.com/serve/MUIFAMyA6BGksOUPEokHuBnEmZRHhYgJ4RXb4BV0gvzN98ymNIvQdTcBFQnRVfviT6yZSFW_R2g1hENBu7p7Afnyv9Kt0unAAbfFU5mMQhBwI-8hQnKf2r1mmRKhUgDLmtk8bpVSZ7kZGpleCPqtYRv8_kkvODL7F4neGLNGgdpFDm9pPzoYpwbQkc0BU5GVOFoq8CSmD0DDvDjG" frameborder="0" scrolling="auto" allowfullscreen="" style="display: block;margin-left: auto;margin-right: auto;max-width: 100%;" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe> <h1 dir="ltr">Understanding Sensor Fusion</h1>Sensor fusion is a multidisciplinary field that integrates data from multiple sensors to derive a more comprehensive and accurate representation of the environment than any individual sensor can provide. Central to this is the principle of Redundancy and Diversity.&nbsp;Redundant sensors, which are multiple units measuring the same parameter, ensure that the system remains resilient against individual sensor failures or inaccuracies. On the other hand, diverse sensors, each capturing different facets of the environment, ensure a comprehensive view. This dual approach not only bolsters the reliability of measurements but also provides a panoramic perspective of the surroundings.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjk3NzI0NzkzMTkxLWltYWdlNi5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 300px;" class="fr-fic fr-dib" alt="sensor-fusion-aircrafts">Fig. 1: The idea of combining data from multiple sensors to improve decision-making, accuracy, and reliability is not new. Historically, sectors like defense and aerospace were among the pioneers in harnessing the power of sensor fusion for tasks such as navigation, target tracking, and situational awareness.<h2 dir="ltr" id="isPasted">Methodologies Driving Fusion</h2>Two methodologies particularly stand out in the realm of sensor fusion are Kalman Filtering and Particle Filtering. They are state estimation techniques that are used to combine noisy measurements from multiple sensors to estimate the true state of a system. The Kalman filter, a recursive algorithm, is tailored for linear dynamic systems. It shines in environments with Gaussian distributed noise, predicting future states based on past measurements. Conversely, Particle filters are the go-to for nonlinear systems. They operate by representing a system's posterior distribution using a set of random samples, offering a more flexible approach to state estimation.<h2 dir="ltr">Challenges in Real-world Implementation</h2>While the promise of sensor fusion is undeniable, it's not without its challenges. Temporal Alignment is a significant hurdle. With sensors operating at different sampling rates, ensuring that data is synchronized is crucial for accurate fusion. Another challenge lies in Spatial Calibration. Given that sensors might have varied orientations or fields of view, aligning them to a common spatial frame is essential.&nbsp;Furthermore, as the number of sensors in a system grows, scalability becomes a concern, demanding efficient algorithms that can handle increased data loads without compromising on processing speed.<h2 dir="ltr">Evaluating Fusion Systems</h2>Lastly, the efficacy of a sensor fusion system is often determined by a set of quality metrics. Accuracy, which measures how close the fused data is to the actual state of the environment, is paramount. However, robustness, the system's resilience against anomalies, and computational efficiency, indicating the system's resource utilization, are equally vital.Further reading:&nbsp;<a href="https://www.wevolver.com/article/what-is-sensor-fusion-everything-you-need-to-know" target="_blank">Sensor Fusion: The Ultimate Guide to Combining Data for Enhanced Perception and Decision-Making</a><h1 dir="ltr">Applications of Sensor Fusion</h1>Sensor Fusion facilitates a variety of applications across various domains by amalgamating data from diverse sensors to derive precise and actionable insights. Here are some of the applications where sensor fusion shines:<ul><li dir="ltr">Autonomous Vehicles:&nbsp;One of the most well-known applications of sensor fusion is in self-driving cars. These vehicles use a combination of cameras, LiDAR, radar, ultrasonic sensors, and more to perceive their surroundings, detect obstacles, and navigate safely. By fusing data from these diverse sensors, autonomous vehicles can operate safely in a wide range of conditions, from bright sunlight to nighttime and from clear weather to fog.</li><li dir="ltr">Robotics:&nbsp;Robots, especially those designed for complex tasks in variable environments, use sensor fusion to combine data from cameras, infrared sensors, tactile sensors, and gyroscopes. This allows them to navigate, interact with objects, and perform tasks with higher precision and reliability.</li></ul><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjk3NzI0ODM3OTc4LWltYWdlNy5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 300px;" class="fr-fic fr-dib" alt="sensor-fusion-wearable">Fig. 2: Sensor fusion is also used in wearable technology, enhancing the capabilities of devices to provide comprehensive insights and improved user experiences.<ul><li dir="ltr">Augmented Reality (AR) and Virtual Reality (VR):&nbsp;AR and VR systems often rely on sensor fusion to track user movements and adjust the displayed visuals accordingly. By combining data from accelerometers, gyroscopes, and cameras, these systems can provide a more immersive and responsive user experience.</li><li dir="ltr">Military and Defense:&nbsp;Advanced defense systems use sensor fusion to combine data from radars, sonars, infrared cameras, and other sensors. This provides a comprehensive situational awareness, enabling quicker and more informed decision-making in complex scenarios.</li><li dir="ltr">Healthcare:&nbsp;Wearable devices that monitor health metrics often use sensor fusion to provide more accurate readings. For instance, a smartwatch might combine data from a heart rate sensor, accelerometer, and gyroscope to better understand a user's activity level and overall health.</li></ul>Other notable applications include environmental monitoring, where sensor fusion aids in forest fire detection and prediction; smart cities, which utilize the technique for traffic management and public safety; agriculture, where precision farming is enhanced through fused data; industrial automation for monitoring equipment health and product quality; and navigation and mapping, where devices like smartphones and drones benefit from improved location accuracy.<h1 dir="ltr">Sensor Fusion with CommonSense addon for Sony Spresense Microcontroller and Edge Impulse</h1>In the rapidly evolving world of IoT and edge computing, the synergy between robust hardware and intelligent software becomes crucial. The <a href="https://www.sensiedge.com/commonsense" target="_blank">SensiEDGE CommonSense</a> add-on, tailored for the <a href="https://developer.sony.com/spresense/" target="_blank">Sony Spresense</a> board, epitomizes this synergy, setting new standards in sensor fusion.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjk3NzI0ODY5OTExLWltYWdlNC5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 300px;" class="fr-fic fr-dib" alt="commonsense-addon-module-for-spreesense">Fig. 3: CommonSense module mounted over a Sony Spresense board. Source:&nbsp;<a href="https://edgeimpulse.com/blog/access-augmented-sensor-capabilities-for-sony-spresense-with-our-new-commonsense-integration" target="_blank" rel="noopener noreferrer">Introducing CommonSense Integration - Edge Impulse</a>The CommonSense add-on is a testament to the advancements in sensor technology within the IoT landscape. Despite its compact and unassuming design, it boasts a plethora of capabilities. Central to its operation is an array of 10 unique sensors that allow the add-on to monitor, measure, and perceive the environment when attached to a Sony Spresense development board.&nbsp;<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjk3NzI0OTI4MzkxLWltYWdlMy5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 300px;" class="fr-fic fr-dib" alt="sensiedge-features">Fig. 4: Infographic showcasing the features of SensiEDGE CommonSense boardIt's adept at capturing a myriad of environmental parameters, from air quality and temperature to humidity. Additionally, its motion tracking capabilities, encompassing an accelerometer, gyroscope, and magnetometer, allow for a broad spectrum of data collection. Augmented by features like audio detection through its microphone and versatile connectivity options via Bluetooth and Wi-Fi, it emerges as an invaluable asset for developers aspiring to craft comprehensive IoT solutions.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjk3NzI0OTUzNDQ4LWltYWdlNS5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 300px;" class="fr-fic fr-dib" alt="sensiedge-commonsense-applications">Fig. 5: &nbsp;CommonSense add-on by SensiEDGE is a compact yet powerful module designed for diverse applicationsThe power of the CommonSense add-on is further magnified when its data is fused, offering a holistic view of the monitored environment or subject. Such consolidated data, when interpreted through machine learning models, paves the way for precise predictions and actionable insights.&nbsp;This is where <a href="https://edgeimpulse.com/" target="_blank">Edge Impulse</a>, a platform renowned for its expertise in embedded machine learning, comes into play. The amalgamation of the CommonSense add-on and Sony's Spresense board with Edge Impulse's software expertise results in a blend of sensor fusion and edge AI. This empowers developers to design, refine, and implement machine learning models right on the device, leveraging AI with no reliance on cloud connectivity.&nbsp;<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjk3NzI0OTg4Mzg3LWltYWdlMi5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 302px;" class="fr-fic fr-dib" alt="edge-impulse-stufio-platform">Fig. 6: Edge Impulse is a developer-first integrated platform for designing, training, testing and deploying edge AI applications.For a deeper dive into this integration and its potential, consider exploring this webinar that accentuates the potential of sensor fusion on the Sony Spresense:<h2><iframe width="640" height="360" src="https://www.youtube.com/embed/5HyGcLK0bQM?&amp;ab_channel=EdgeImpulse&amp;wmode=opaque&amp;rel=0&amp;enablejsapi=1&amp;origin=https://www.wevolver.com" frameborder="0" allowfullscreen="" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe>Conclusion</h2>The integration of SensiEDGE CommonSense add-on with Sony Spresense and Edge Impulse makes for a powerful combination that enhances the reliability, accuracy, and efficiency of data interpretation in various applications, all on the edge.&nbsp;Leveraging advanced hardware and intelligent software promises to drive innovation, offering a more holistic and nuanced understanding of our environment. In essence, this collaboration is set to redefine the boundaries of sensor fusion, paving the way for groundbreaking advancements in the interconnected world of modern technology.<h1 dir="ltr">About the sponsor: Edge Impulse</h1><a href="https://www.edgeimpulse.com/" target="_blank">Edge Impulse</a> is the leading development platform for embedded machine learning, used by over 1,000 enterprises across 200,000 ML projects worldwide. We are on a mission to enable the ultimate development experience for machine learning on embedded devices for sensors, audio, and computer vision, at scale.&nbsp;From getting started in under five minutes to MLOps in production, we enable highly optimized ML deployable to a wide range of hardware from MCUs to CPUs, to custom AI accelerators. With Edge Impulse, developers, engineers, and domain experts solve real problems using machine learning in embedded solutions, speeding up development time from years to weeks. We specialize in industrial and professional applications including predictive maintenance, anomaly detection, human health, wearables, and more.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjk3NzI1MTI5ODY4LWltYWdlMS5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 300px;" class="fr-fic fr-dib" alt="edge-impulse"><h2 dir="ltr"><iframe width="540" height="600" src="https://c8056756.sibforms.com/serve/MUIFAMyA6BGksOUPEokHuBnEmZRHhYgJ4RXb4BV0gvzN98ymNIvQdTcBFQnRVfviT6yZSFW_R2g1hENBu7p7Afnyv9Kt0unAAbfFU5mMQhBwI-8hQnKf2r1mmRKhUgDLmtk8bpVSZ7kZGpleCPqtYRv8_kkvODL7F4neGLNGgdpFDm9pPzoYpwbQkc0BU5GVOFoq8CSmD0DDvDjG" frameborder="0" scrolling="auto" allowfullscreen="" style="display: block;margin-left: auto;margin-right: auto;max-width: 100%;" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe> </h2><h2 dir="ltr">References</h2>[1] Mitchell, H. (2007). Multi-Sensor Data Fusion. Springer, Berlin, Heidelberg. <a href="https://doi.org/10.1007/978-3-540-71559-7_1" target="_blank">https://doi.org/10.1007/978-3-540-71559-7_1</a>[2] Chui, C.K., Chen, G. (2017). Kalman Filtering. Springer, Cham.&nbsp;<a href="https://doi.org/10.1007/978-3-319-47612-4_1" target="_blank">https://doi.org/10.1007/978-3-319-47612-4_1</a>[3] ElProCus. What is Sensor Fusion: Working &amp; Its Applications. ElProCus. [Online]. Available:&nbsp;<a href="https://www.elprocus.com/sensor-fusion/" target="_blank">https://www.elprocus.com/sensor-fusion/</a>[4] Sony, Spresense 6-core microcontroller board with ultra-low power consumption [Internet]. Available from:&nbsp;<a href="https://developer.sony.com/spresense/" target="_blank">https://developer.sony.com/spresense/</a>[5] SensiEDGE, CommonSense: Sensor AI Board [Internet]. Available from:&nbsp;<a href="https://www.sensiedge.com/commonsense" target="_blank">https://www.sensiedge.com/commonsense</a>

Sensor fusion enables the seamless integration of data from multiple sensors, paving the way for advanced Edge AI implementations that optimize real-time processing, enhance decision-making, and boost system responsiveness in dynamic environments.