Some 80% of weather radiosondes – remote measurement instruments containing plastic, batteries and electronic parts – end up lost in nature after one flight. But a startup created by an EPFL master’s student is set to change that with a new, ultra-lightweight “glidersonde” that can automatically return to where it was launched.<iframe width="640" height="360" src="https://www.youtube.com/embed/U_GkseyejII?&amp;wmode=opaque&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>It’s standard practice in many countries to launch radiosondes carried by weather balloons twice a day. These instruments climb for two hours towards the stratosphere, collecting data on wind speed and direction, temperature, pressure and humidity at various altitudes – critical information for generating weather forecasts and improving climate models. The weather balloons are typically inflated with hydrogen, and when they reach an altitude of around 35,000 meters, the low atmospheric pressure causes them to burst and drop the radiosondes. As they fall to the ground, the instruments are pushed by wind currents and sometimes land in water or very remote areas. Of the approximately 600,000 radiosondes launched around the world each year, some 80% are never found. Yohan Hadji, an EPFL master’s student, has developed a solution to this problem – a new type of radiosonde that can automatically fly back to a predetermined spot. His device was tested by the Swiss Federal Office of Meteorology and Climatology (MeteoSwiss) this summer, and weather offices in other countries are taking an interest, too. “France, the UK, Germany, Croatia and Canada also want to try it out,” says Hadji, who just created a startup called R2Home.<h2>Algorithms adjust for wind measurements taken during the ascent</h2>Hadji’s radiosondes are attached to small foam gliders equipped with a guidance system. The entire unit weighs a mere 250 grams and is lifted into the air by a weather ballon. “My device consists of a standard radiosonde inside a wing-shaped box,” he says. “It’s small and light enough to classify as a conventional radiosonde under the FOCA [Swiss Federal Office of Civil Aviation] classification.” The guidance system uses an algorithm to calculate the optimal trajectory for returning to the point where the radiosonde was launched or to another site programmed into its GPS. Developing it was a real technical challenge given the glider’s ultra-lightweight material. “I had to create an algorithm that could factor in wind measurements taken during the ascent,” says Hadji. “At some altitudes, the wind can reach speeds of over 200 km/h!”Hadji presented his invention to meteorologists and to experts at the FOCA, who were interested in its feasibility and reliability. The FOCA gave him an opportunity to run his first test flights under real-world conditions. In all, Hadji’s glidersondes have been tested approximately 60 times in Switzerland, including around a dozen times at the MeteoSwiss weather station in Payerne this summer. During each test flight, the glider successfully returned to within a 15-meter radius of the desired location. “The initial results are very promising,” says Yves-Alain Roulet, head of the Measurement Technology Division at MeteoSwiss. “Now we need to test the system’s performance under more complicated weather conditions.”Switzerland leads the pack when it comes to recovering lost radiosondes: approximately 75% of them are found, compared with a global average of 20%. “That’s because we have a highly active community of radiosonde chasers,” says Hadji. Yet less than 1% of the recovered radiosondes are actually used again, since the temperature and humidity sensors tend to get damaged after spending so much time exposed to the elements. “My device can help make sure that doesn’t happen, so that the radiosondes can be used for numerous flights,” says Hadji.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1731976749545-6af30311.jpg" style="width: 100%px;" class="fr-fic fr-dib">Hadji’s radiosonde may be able to return to where it was launched, but is it effective at its primary function – taking accurate measurements? MeteoSwiss ran comparative tests to find out. “Our initial tests found no significant difference between the radiosonde embedded in the R2Home glider and a standard radiosonde,” says Roulet. “But further testing is needed here too, including in weather conditions representative of all four seasons.” These encouraging results paved the way for Hadji to attend the World Meteorological Organization’s Technical Conference on Meteorological and Environmental Instruments and Methods of Observation (TECO) this year, held in Vienna, Austria, in late September.Hadji, now 20, began working on his invention in 2019 while a high-school student in Grenoble. Since then he’s been advancing through the various stages of product development at a rapid pace – despite encountering some headwinds along the way. “For instance, until 2023 my idea was to attach the radiosonde to a parachute rather than a foam glider,” he says. “But that design would have been problematic at very high altitudes.” He may further develop that approach sometime in the future too, because he believes “it could be an attractive option for heavier and more expensive payloads.”<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1731976766208-a3551373.jpg" style="width: 100%px;" class="fr-fic fr-dib"><h2 id="isPasted">Running tests with foreign weather offices</h2>Since 2022, Hadji has benefited from a number of local support programs for entrepreneurs: EPFL’s Blaze program, the Foundation for Innovation and Technology, the Sylvie Rusconi Foundation and, more recently, Venture Kick. His startup was recently acquired by Meteomatics, a Swiss company that’s the world’s leading supplier of weather information. For now, Hadji is focused on preparing for the tests with foreign weather offices. The many radiosonde chasers around the world could soon find themselves coming home empty-handed.<hr>References<a href="https://www.meteoswiss.admin.ch/about-us/meteoswiss-blog.html" target="_blank" rel="noopener">MétéoSuisse on R2Home</a>

Some 80% of weather radiosondes – remote measurement instruments containing plastic, batteries and electronic parts – end up lost in nature after one flight. An EPFL student is set to change that with a new, ultra-lightweight “glidersonde” that can automatically return to where it was launched.

New type of weather radiosonde can find its way back

<a href="https://www.projectceti.org/" target="_blank">Project CETI</a> (Cetacean Translation Initiative) aims to collect millions to billions of high-quality, highly contextualized vocalizations in order to understand how sperm whales communicate. But finding the whales and knowing where they will surface to capture the data is challenging — making it difficult to attach listening devices and collect visual information.&nbsp;Today, a Project CETI research team led by&nbsp;<a href="https://gil.seas.harvard.edu/" target="_blank">Stephanie Gil</a>, Assistant Professor of Computer Science at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS), have proposed a new reinforcement learning framework with autonomous drones to find sperm whales and predict where they will surface.&nbsp;The research is published in&nbsp;<a href="https://www.science.org/doi/10.1126/scirobotics.adn7299" target="_blank">Science Robotics.&nbsp;</a>This new study uses various sensing devices, such as Project CETI aerial drones with very high frequency (VHF) signal sensing capability that leverage signal phase along with the drone’s motion to emulate an ‘antenna array in air’ for estimating directionality of received pings from CETI’s on-whale tags. It demonstrates that it’s possible to predict when and where a whale may surface by using these various sensor data as well as predictive models of sperm whales dive behavior. With that information, Project CETI can now design algorithms for the most efficient route for a drone to rendezvous—or encounter—a whale at the surface. This also opens up possible conservation applications to help ships avoid striking whales while at the surface.Presenting the Autonomous Vehicles for whAle Tracking And Rendezvous by remote Sensing, or AVATARS framework, this study jointly develops two interrelated components of autonomy and sensing: autonomy, which determines the positioning commands of the autonomous robots to maximize visual whale encounters; and sensing, which measures the Angle-of-Arrival (AOA) from whale tags to inform the decision-making process. Measurements from our autonomous drone to surfaced tags, acoustic AOA from existing underwater sensors, and whale motion models from previous biological studies of sperm whales are provided as inputs to the AVATARS autonomous decision-making algorithm, which in turn aims to minimize missed rendezvous opportunities with whales.&nbsp;AVATARS is the first co-development of VHF sensing and reinforcement learning decision-making for maximizing rendezvous of robots and whales at sea. A well-known application of time-critical rendezvous is used with rideshare apps, which uses real-time sensing to note the dynamic paths and positions of drivers and potential riders. When a rider requests a ride, it can assign a driver to rendezvous with the rider as efficiently and as timely as possible. Project CETI’s case is similar in that they are real-time tracking the whale, with the goal of coordinating the drone’s rendezvous to meet the whale at the surface.&nbsp;This research advances Project CETI's goal of obtaining millions to billions of high-quality, highly contextualized whale vocalizations. The addition of diverse types of data will improve location estimates and routing algorithms—helping Project CETI meet that goal more efficiently.“I’m excited to contribute to this breakthrough for Project CETI. By leveraging autonomous systems and advanced sensor integration, we’re able to solve key challenges in tracking and studying whales in their natural habitats. This is not only a technological advancement, but also a critical step in helping us understand the complex communications and behaviors of these creatures,” said Gil. “This research is a major milestone for Project CETI’s mission. We can now significantly enhance our ability to gather high-quality and large-scale dataset on whale vocalizations and the associated behavioral context, putting us one step closer to better listening to and translating what sperm whales are saying,” said David Gruber, Founder and Lead of Project CETI.“'This research was an amazing opportunity to test our systems and algorithms in a challenging marine environment. This interdisciplinary work, that combines wireless sensing, artificial intelligence and marine biology, is a prime example of how robotics can be part of the solution for further deciphering the social behavior of sperm whales,” said&nbsp;Ninad Jadhav, Harvard University PhD candidate and first author on the paper.“This project provides an excellent opportunity to test our algorithms in the field, where robotics and artificial intelligence can enrich data collection and expedite research for broader science in language processing and marine biology, ultimately protecting the health and habitat of sperm whales,” said Sushmita Bhattacharya, a postdoctoral researcher in Gil's REACT Lab at SEAS.

Project CETI and Harvard have established a new reinforcement learning framework for rendezvous with whales using autonomous robots, combining sensing from diverse sensor streams

New methods for whale tracking and rendezvous using autonomous robots

<h1 dir="ltr" id="isPasted">Context and Importance</h1>The current market size of Cloud computing is estimated to be USD 0.68 trillion, and it is growing at a very rapid pace. According to some estimations, by 2029, the cloud computing market will reach USD 1.44 trillion with a compound annual growth rate of 16.40%. 1&nbsp;As cloud computing adoption has accelerated, so too has the energy demand to power the massive data centers that host these services. These energy demands have significant environmental implications, contributing to increased carbon emissions and raising concerns about the sustainability of the digital future. In this regard, the concept of Green Cloud has emerged to be an imperative that mitigates these concerns.The aim of this article is to explain the Green Cloud, its underlying principles, and how engineers and organizations can implement sustainable practices in cloud operations.<h1 dir="ltr">Defining the Green Cloud</h1><h2 dir="ltr">What is the Green Cloud?</h2>The Green Cloud means integrating sustainable practices into cloud computing operations by focusing on energy-efficient, environmentally-friendly cloud services that minimize carbon emissions and reduce resource consumption. Traditionally, cloud computing primarily focuses on factors like scalability, flexibility, and cost-efficiency without environmental aspects as a primary concern. However, in the case of Green Cloud computing, along with the aforementioned factors, the objective is to create a more eco-conscious digital infrastructure that contributes to long-term environmental preservation and seeks to manage resources sustainably via energy usage optimization and e-waste reduction.<h2 dir="ltr">Key Environmental Issues in Cloud Computing</h2>Data centers are considered the core of cloud computing; however, they consume vast amounts of energy that leads to an excess carbon footprint in the environment. The power required to operate servers, cool systems, and manage data has significant environmental consequences, especially when fueled by non-renewable energy sources like coal and natural gas. Moreover, as the hardware and cooling systems become outdated, data centers also generate significant e-waste that can contribute to resource depletion and hazardous waste production.<h1 dir="ltr">Why the Green Cloud Matters</h1><h2 dir="ltr">Impact of Traditional Cloud Practices</h2>The energy consumption of traditional cloud computing practices is staggering. For instance, the three hundred thousand servers of Microsoft's Chicago data center alone consume 23.5% of the USA's total coal-generated electric power. Similarly, Google has over one million servers worldwide that also consume a lot of energy.&nbsp;2The energy consumption of data centers is expected to further increase, driven by the exponential growth of data. Without intervention, this could lead to even greater carbon emissions, placing unsustainable pressure on global power grids and ecosystems. It is projected that the proportion of global carbon dioxide emissions attributed to information and communication technologies (ICTs) has grown from 1.3% in 2002 to 2.3% by 2020.&nbsp;3&nbsp;Therefore, it is imperative to shift towards sustainable cloud operations that prioritize environmental sustainability.<h1 dir="ltr">Principles of a Green Cloud</h1><h2 dir="ltr">Energy Efficiency</h2>The principles of a Green Cloud revolve around minimizing environmental impact by using energy-efficient hardware and optimizing server infrastructure. Modern data centers are adopting energy-efficient processors and low-power servers designed to reduce electricity consumption. To improve energy efficiency, one key strategy can be utilizing virtualized environments, where multiple virtual servers can run on a single physical machine enabling workload consolidation, which means that data centers can handle more tasks with fewer resources, significantly reducing idle server energy consumption.<h2 dir="ltr">Renewable Energy Adoption</h2>Currently cloud processing is heavily dependent on non-renewable energy sources, as evident through the example of Microsoft's Chicago data center using a quarter of the USA's coal-generated energy. Powering data centers with renewable energy is a critical step in achieving sustainable cloud computing as renewable energy sources, such as solar, wind, and hydroelectric power, offer a clean alternative to fossil fuels that can help reduce the carbon footprint of cloud operations.&nbsp;In this regard, major cloud providers like Google and Microsoft are prioritizing sustainability by committing to renewable energy use and reducing their carbon footprints. For instance, Microsoft Azure has set a goal to achieve carbon-negative operations by 2030, while Google plans to run entirely carbon-free.&nbsp;4<h2 dir="ltr">Carbon Reduction Techniques</h2>Another important principle is to utilize carbon reduction techniques, including utilizing low-power processors, energy management software, and cooling system optimizations that minimize the energy required to maintain ideal operating temperatures in data centers. For instance, open-source tools like <a href="https://www.cloudcarbonfootprint.org/">Cloud Carbon Footprint</a> are designed to measure, track, and reduce carbon emissions from cloud usage. This tool generates metrics and projections for potential carbon reductions, which can be communicated to employees, investors, and other relevant stakeholders. 5Similarly, carbon offset programs that enable cloud providers to invest in environmental projects that compensate for their carbon emissions are also great initiatives toward Green Cloud.&nbsp;<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1730724156554-Screenshot+2024-09-25+115819.png" width="624" height="291" class="fr-fic fr-dii">Screenshot from the Cloud Carbon Footprint tool. Credit: cloudcarbonfootprint<h1 dir="ltr">Technological Approaches to Achieving the Green Cloud</h1><h2 dir="ltr">Cloud Infrastructure Optimization</h2>Technological approaches to achieve a Green Cloud focus on optimizing cloud infrastructure to minimize energy consumption. For instance, techniques such as power-efficient cooling systems and improved server designs reduce energy usage in data centers.&nbsp;Moreover, data centers can minimize their environmental impact by using techniques like resource scheduling and workload management to ensure that resources are not wasted on idle tasks. In this regard, automation also plays a vital role as automated systems can intelligently manage server activity, powering down unused servers or scaling resources based on real-time needs.&nbsp;<h2 dir="ltr">Edge Computing and Distributed Data Centers</h2>Edge computing is another way of achieving Green Cloud, as it reduces long-distance data transfer requirements by processing data closer to its source. This approach of handling computation locally lowers bandwidth usage and the power required for network operations as well as reducing energy costs.&nbsp;Similarly, distributing data across geographically diverse data centers further helps reduce energy intensity by utilizing regional power resources more effectively. These data centers are strategically placed to take advantage of local renewable energy sources and climate conditions for efficient cooling.<h2 dir="ltr">Virtualization and Containerization</h2>Virtualization and containerization are critical technologies to achieve Green Cloud. Container-based architectures contribute to energy savings by allowing multiple applications to share the same operating system while running in isolated environments with minimal overhead. Containerization allows workloads to run on fewer servers, which reduces the energy required for powering and cooling data centers. Virtualization and containerization help cloud providers achieve significant energy savings by optimizing the utility of available hardware while maintaining flexibility and scalability.<h1 dir="ltr">Engineering Challenges in Implementing Green Cloud Practices</h1><h2 dir="ltr">Balancing Performance and Sustainability</h2>Implementing Green Cloud practices presents engineering challenges in balancing performance and sustainability. For instance, one significant challenge is the trade-off between energy savings and cloud performance, as energy-saving measures, such as reducing power to idle servers, can sometimes introduce latency or lower performance.&nbsp;Exploring new technologies and architectures that can improve energy efficiency without sacrificing performance is imperative to tackle this challenge. In this regard, adopting hybrid architectures that combine traditional and cloud-native solutions can provide the flexibility needed to achieve both sustainability and high performance.<h2 dir="ltr">Cost vs. Environmental Benefit</h2>The economic challenges of adopting Green Cloud solutions can be significant, especially for smaller organizations, since the upfront costs of transitioning to energy-efficient hardware, renewable energy sources, and optimized cloud infrastructure can be substantial. However, long-term cost savings from reduced energy consumption often outweigh these initial investments.Quantifying the return on investment (ROI) for Green Cloud investments, however, adds another layer of complexity. Organizations must balance immediate financial implications against long-term environmental benefits by developing metrics that accurately assess energy savings, reduced operational costs, and potential regulatory incentives. Businesses can utilize cost-benefit analyses and lifecycle assessments to better understand the financial and environmental impact of their investments.<h1 dir="ltr">Real-World Examples and Best Practices</h1><h2 dir="ltr">Case Studies of Green Cloud Implementation</h2>Major cloud providers are leading the charge toward sustainable cloud computing. For instance, in 2023, for the seventh year in a row, Google matched 100% of its electricity usage with renewable energy. Moreover, Google is aiming for 24/7 carbon-free energy across all its global operations by 2030. 6&nbsp;Similarly, AWS achieved to match 100% of their consumed electricity with renewable energy in 2023, and they are aiming to achieve their target of net-zero carbon by 2040.&nbsp;7&nbsp;Microsoft has also made a significant commitment by targeting carbon negativity by 2030.&nbsp;4Smaller companies are also aiming to achieve sustainable cloud operations. For instance, Ecosia is a search engine that donates 80% of its profits to reforestation projects worldwide.8&nbsp;Similarly, GreenGeeks is a web hosting company that claims to be 300% green. They use renewable energy credits to offset their carbon footprint and support wind energy projects.&nbsp;9<h1 dir="ltr">The Future of the Green Cloud</h1><h2 dir="ltr">Trends in Sustainable Cloud Computing</h2>AI and machine learning can contribute a lot to the future of Green Cloud for energy optimization. With AI and machine learning, data centers will be able to analyze vast amounts of operational data, identify inefficiencies, and dynamically adjust resource allocation to minimize energy consumption while maintaining performance.&nbsp;Another crucial trend for the future of Green Cloud is the rise of zero-carbon data centers, which aim to eliminate greenhouse gas emissions from their operations. Top companies are aiming to adopt climate-positive operations and go beyond carbon neutrality towards carbon-negative by actively contributing to carbon removal and climate restoration.<h1 dir="ltr">Conclusion</h1>The Green Cloud is an important step toward addressing the environmental impact of cloud computing. Cloud providers can significantly reduce their carbon footprints via energy-efficient hardware, renewable energy sources, and carbon reduction techniques.&nbsp;The Green Cloud is not just a trend but a necessary transformation in how we approach digital infrastructure. In this regard, engineers have a responsibility and a critical role in advancing these technologies and ensuring that cloud computing continues to grow in an environmentally responsible manner. The future of computing can become both powerful and environmentally friendly if Green Clooud practices are adopted widely.<h1 dir="ltr">References</h1><ol><li dir="ltr">Cloud Computing Market Size &amp; Share Analysis - Growth Trends &amp; Forecasts (2024 - 2029) [Online]. Mordor Intelligence. Available at: <a href="https://www.mordorintelligence.com/industry-reports/cloud-computing-market">https://www.mordorintelligence.com/industry-reports/cloud-computing-market</a> (Accessed on September 24, 2024)</li><li dir="ltr">Bharany, S., et al,. (2022). A systematic survey on energy-efficient techniques in sustainable cloud computing. Sustainability. <a href="https://doi.org/10.3390/su14106256">https://doi.org/10.3390/su14106256</a></li><li dir="ltr">Radu, L. D. (2017). Green cloud computing: A literature survey. Symmetry. <a href="https://doi.org/10.3390/sym9120295">https://doi.org/10.3390/sym9120295</a></li><li dir="ltr">Who has the greenest cloud? The most sustainable cloud tech in 2024 [Online]. Hive. Available at: <a href="https://www.hivenet.com/post/who-has-the-greenest-cloud-the-most-sustainable-cloud-tech-in-2024">https://www.hivenet.com/post/who-has-the-greenest-cloud-the-most-sustainable-cloud-tech-in-2024</a> (Accessed on September 24, 2024)</li><li dir="ltr">Cloud Carbon Emissions Measurement and Analysis Tool [Online]. Cloud Carbon Footprint. Available at: &nbsp;<a href="https://www.cloudcarbonfootprint.org/">https://www.cloudcarbonfootprint.org/</a> (Accessed on September 24, 2024)</li><li dir="ltr">24/7 Carbon-Free Energy by 2030. [Online] Google Data Centers. Available at: <a href="https://www.google.com/about/datacenters/cleanenergy/">https://www.google.com/about/datacenters/cleanenergy/</a> (Accessed on September 24, 2024)</li><li dir="ltr">AWS enables sustainability solutions. [Online]. AWS. Amazon. Available at: &nbsp;<a href="https://aws.amazon.com/sustainability/">https://aws.amazon.com/sustainability/</a> (Accessed on September 24, 2024)</li><li dir="ltr">Ellie Hutchin (2017) Ecosia. The Search Engine That's Saving The World! [Online] The Great Projects. Available at: &nbsp;<a href="https://www.thegreatprojects.com/blog/the-search-engine-that-s-saving-the-world">https://www.thegreatprojects.com/blog/the-search-engine-that-s-saving-the-world</a> (Accessed on September 24, 2024)</li><li dir="ltr">Web Hosting that's fast, secure &amp; eco-friendly. [Online] Green Geeks. Available at: &nbsp;<a href="https://www.greengeeks.com/">https://www.greengeeks.com/</a> (Accessed on September 24, 2024)</li></ol>

The aim of this article is to explain the Green Cloud, its underlying principles, and how engineers and organizations can implement sustainable practices in cloud operations.

Understanding the Green Cloud: An overview of sustainable cloud computing

Large Language Model (LLM) training involves teaching AI models to understand and generate human-like text by processing vast amounts of data, significantly enhancing their language comprehension and production capabilities.

LLM Training: Mastering the Art of Language Model Development

An interview with Adam White from Infineon on Powering the Next Generation of AI Data Centers.

There is No AI Without Power

Surgical instruments, sponges, or other objects that are inadvertently left inside a patient's body following a procedure are referred to as retained surgical bodies. Despite stringent safety protocols and advancements in surgical practices, these incidents can still occur, posing serious risks to patient health and safety.<iframe width="640" height="360" src="https://www.youtube.com/embed/1k0pfPwzTw4?&amp;wmode=opaque&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>Several factors contribute to the occurrence of retained surgical bodies. One common factor is the hectic nature of surgical environments, where numerous instruments and items are used during procedures. High-stress situations, time pressure, and distractions can lead to oversight, causing medical teams to unintentionally leave objects inside patients. Additionally, during complex surgeries or emergency situations, maintaining an accurate count of surgical instruments and items may prove challenging, increasing the likelihood of retention.<a href="https://form.jotform.com/242533284499365"><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1728891364784-5+Rising+Trends+for+AI+Adoption+in+Manufacturing+%283%29.png" style="width: 100%px;" class="fr-fic fr-dib"></a>There are a number of practices that are intended to prevent the occurrence of retained surgical bodies. The most fundamental measure is the implementation of standardized counting procedures before, during, and after surgery. Surgical teams meticulously count and document all instruments and items used, cross-verifying with a designated checklist. The use of radio frequency identification technology and barcoding systems on surgical instruments has also been introduced to enhance tracking and minimize human error.Unfortunately, errors do still occur. And those errors can be very costly — complications arising from retained surgical bodies can be severe and potentially life-threatening. Patients may experience localized pain, infection, inflammation, and, in some cases, damage to surrounding organs or tissues. The foreign object can lead to the formation of abscesses or contribute to the development of systemic infections. In extreme cases, retained surgical bodies may necessitate additional surgeries to remove the object, which poses further risks and complications.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1728891383846-image3.png" style="width: 100%px;" class="fr-fic fr-dib">AI surgical tool tracker: the finished device<h3 id="isPasted">Building a smart solution</h3>In an effort to remove human error from the equation, Eivind Holt has developed an automated system that can objectively assess if the entire surgical inventory has been accounted for at the end of a procedure. This system never suffers from stress in emergency situations, and never gets distracted from the task at hand, which could make it the ideal solution to the problem. The initial prototype has been implemented as a wearable device that can recognize a variety of surgical instruments as it sees them so that medical professionals can later ensure that they are all accounted for.Building a device like this can quickly get complicated. To be practical, a wearable device must be small and capable of operating for long periods of time on battery power. Designing an object detection algorithm that can efficiently run on such a tiny hardware platform also presents a number of issues, as computer vision-related tasks are often very resource-intensive. Moreover, for a typical person, collecting enough images of surgical instruments to train the object detection model can be challenging.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1728891405169-image2.png" style="width: 100%px;" class="fr-fic fr-dib">Surgical instruments used to collect the initial training dataset<h3 id="isPasted">Choosing hardware</h3>Holt was able to work through these problems by carefully selecting a set of tools that can address each of the challenges. On the hardware front, an Arduino Nicla Vision was chosen. At just 22.86 mm x 22.86 mm, it can easily clip on a shirt without getting in the way. With its built-in image sensor and an Arm Cortex M7 processor with 1 MB of RAM, it has the power to run some fairly sophisticated algorithms. Object detection algorithms, however, can easily stretch well beyond the capabilities of this device, so Holt utilized <a href="https://docs.edgeimpulse.com/docs/edge-impulse-studio/learning-blocks/object-detection/fomo-object-detection-for-constrained-devices" rel="noreferrer">Edge Impulse’s FOMO object detection</a> algorithm to not only make the development and deployment process simple, but also to optimize the model for the Nicla Vision hardware.<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%2F1728891435051-1728891435051.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>With the basic game plan finalized, Holt got together a few basic instruments, like scalpels and forceps, then took a set of about 600 images with the Nicla Vision. After these images were uploaded to Edge Impulse and used to train the object detection model, it quickly became clear that something was wrong. The object detection accuracy rate was too low for the model to be of any use. After some investigation, Holt determined that reflective objects were the problem. All of the reflections changed the features of the objects that the model detected. And unfortunately, a large percentage of surgical instruments have a reflective chrome surface.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1728891460938-image5.png" style="width: 100%px;" class="fr-fic fr-dib">Generating synthetic data with NVIDIA’s Omniverse Replicator<h3 id="isPasted">More data needed</h3>Collecting a larger, more diverse dataset would likely resolve this issue, but that can be very challenging, especially in the context of surgical equipment. So, Holt came up with the idea of <a href="https://docs.edgeimpulse.com/experts/image-projects/nvidia-omniverse-replicator" rel="noreferrer">utilizing NVIDIA’s Omniverse Replicator to quickly generate a large, varied dataset</a> to further train the model. With this tool, 3D models of a wide variety of surgical instruments could be imported into a scene, then Replicator will move them around, adjust angles, lighting, and other factors to produce synthetic images that can be leveraged in model training. In this way, over 30,000 training images were produced.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1728891479091-synthetic-data.png" style="width: 100%px;" class="fr-fic fr-dib">Synthetic image from Omniverse ReplicatorAfter uploading this new set of data to Edge Impulse, Holt kicked off another round of model training. The results were much more favorable. The average object detection accuracy had climbed to 85.5%, which is more than good enough to prove the concept. This is an important reminder just how important good training data is — without the boost from Omniverse Replicator, this project may have been abandoned as too impractical.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1728891501209-image4.png" style="width: 100%px;" class="fr-fic fr-dib">To put the finishing touches on the project, the object detection pipeline was deployed from Edge Impulse to the physical device. It was noted that the model can be deployed as an Arduino-compatible library, which allows for total customization of the application running on the hardware. In this way, the object detector could wirelessly communicate with a phone or tablet app, for example, via Bluetooth Low Energy to provide information about the surgical instruments used during a procedure. As a final step, Holt 3D-printed a small housing for the Nicla Vision and a LiPo battery that can be clipped to a shirt.It is amazing to think that with a bit of refinement, this inexpensive and relatively simple project could become a tool that saves lives in the future. Holt included a great deal of additional information in the <a href="https://docs.edgeimpulse.com/experts/featured-machine-learning-projects/surgery-inventory-synthetic-data">project write-up</a>, so be sure to take a look if you have any lingering questions or would like to get started building a wearable object detector for your own application.<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>

Several factors contribute to the occurrence of retained surgical bodies. One common factor is the hectic nature of surgical environments, where numerous instruments and items are used during procedures.

No Scalpel Left Behind: Tracking Surgical Tools with AI and Synthetic Data from NVIDIA Omniverse

We are proud to announce that synthetic data generation&nbsp;is now available inside the Edge Impulse platform, enabling&nbsp;a new and efficient way to work with LLM-generated images, audio, and voice to enhance your edge AI models.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzI4ODg5OTU3MzAzLWltYWdlMy0xLnBuZyIsImVkaXRzIjp7InJlc2l6ZSI6eyJ3aWR0aCI6OTUwLCJmaXQiOiJjb3ZlciJ9fX0=" style="width: 100%px;" class="fr-fic fr-dib" alt="">Quickly access and generate synthetic data in Edge ImpulseTraining effective machine learning models requires a lot of accurate data. However, collecting and curating this data to reflect real-world scenarios is often challenging and expensive. The time, effort, and costs involved can be substantial, posing significant barriers. One option is using synthetic data, which has advanced to the point that all types of it are now viable as a part of datasets used for AI training.&nbsp;<a href="https://form.jotform.com/242533284499365"><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzI4ODkwMTE2OTczLTUgUmlzaW5nIFRyZW5kcyBmb3IgQUkgQWRvcHRpb24gaW4gTWFudWZhY3R1cmluZyAoMykucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 100%px;" class="fr-fic fr-dib"></a>As of today, we have integrated with three different GenAI solutions, <a href="https://openai.com/index/dall-e/">Dall-E</a> to generate images, <a href="https://openai.com/index/whisper/">Whisper</a> for creating human speech elements, and <a href="https://elevenlabs.io/">ElevenLabs</a> to generate audio sound effects.&nbsp;<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzI4ODkwMTQ1NTc0LWltYWdlMi0xLnBuZyIsImVkaXRzIjp7InJlc2l6ZSI6eyJ3aWR0aCI6OTUwLCJmaXQiOiJjb3ZlciJ9fX0=" style="width: 100%px;" class="fr-fic fr-dib">Integrated data generation from multiple applicationsAnd new foundation models and services are getting released every week! In order to make these integrations scalable, every user belonging to an Edge Impulse organization can also create their own synthetic data generation integrations. <a href="https://docs.edgeimpulse.com/docs/tutorials/ml-and-data-engineering/generate-synthetic-datasets/synthetic-data-tab">See our documentation</a> for more details.The direct integration of LLM-based data generation in Edge Impulse is available now for Enterprise Plan users and <a href="https://edgeimpulse.com/pricing">Professional Plan users</a>; you can access this new feature directly in your Edge Impulse projects under the “data acquisition” section, alongside Dataset, Data explorer, and Data sources options.The Synthetic Data menu lists the GenAI transformation blocks, both public and private, that have synthetic data capabilities. &nbsp;Within the new Synthetic Data section, the user can add and refine their prompts quickly and efficiently. The output, including images, are then displayed, allowing users to quickly evaluate and refine their prompts until they get the desired data set, and easily delete unwanted or incorrect data samples.This iterative workflow will make it much clearer to determine the right prompts for generating data. Additionally, any data that is not deleted will automatically be added to the project, ensuring seamless data management as you continue to build and refine your edge AI model.&nbsp;<table><tbody><tr><td><h3>New whitepaper: Discover the 5 AI Trends Transforming Manufacturing</h3><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzI4ODkwNDMwNjUwLTE3Mjg4OTA0MzA2NTAucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" 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><h2>Sample Workflows</h2>To generate synthetic data, navigate to your project in Edge Impulse. Click on “Data acquisition” in the left hand menu and Select the "Synthetic Data" tab in the top nav.Here are the steps for the currently supported Gen-AI functions:<h4>Synthetic Images</h4><ul><li>From the drop-down, select DALL-E 3 Synthetic Image Generator</li><li>Enter a prompt: For example,&nbsp;“People wearing hard hats”</li><li>Click “Generate data” to create the images&nbsp;</li><li>Review, iterate, and prune images from the newly generated samples</li></ul>New samples are automatically saved on your dataset<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzI4ODkwMTc5MjUwLWltYWdlMS0xLnBuZyIsImVkaXRzIjp7InJlc2l6ZSI6eyJ3aWR0aCI6OTUwLCJmaXQiOiJjb3ZlciJ9fX0=" style="width: 100%px;" class="fr-fic fr-dib">The Dall-E image generator functionality<h4 id="isPasted">Generate Human Speech</h4><ul><li>From the drop-down, select Whisper Synthetic Voice Generator</li><li>Enter a text prompt you want converted to digital speech. For example, "Hello, Edge!"</li><li>Click "Generate" to create the speech data</li><li>Review and save the generated audio files</li></ul><h4>Generate Audio</h4><ul><li>Select ElevenLabs Synthetic Audio Generator&nbsp;(<a href="https://www.edgeimpulse.com/blog/create-sound-datasets-generative-ai/" rel="noreferrer">more info here</a>)</li><li>Enter prompt. For example: “Glass breaking impact”</li><li>Click “Generate” to create the sound effects</li><li>Review, iterate, and eventually delete the newly generated samples</li><li>New samples are automatically saved on your dataset&nbsp;</li></ul><iframe width="640" height="360" src="https://www.youtube.com/embed/vnyFk58qpf4?&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;&nbsp;</iframe>See the new Synthetic Data integration in use with the new ElevenLabs integration video<a href="https://docs.edgeimpulse.com/docs/tutorials/ml-and-data-engineering/generate-synthetic-datasets/synthetic-data-tab">Read the docs page</a> for more information on all three options.This new capability empowers users to streamline the process of generating and refining LLM-based prompts to create the desired data set. It will provide an efficient workflow for building models using synthetic data and make it easier for developers to push custom Gen-AI transformation blocks for their optimal AI model generation.&nbsp;<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:</div></div><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">&nbsp;&nbsp; </iframe>

We are proud to announce that synthetic data generation is now available inside the Edge Impulse platform, enabling a new and efficient way to work with LLM-generated images, audio, and voice to enhance your edge AI models.

Introducing Synthetic Data Generation in Edge Impulse

Smart manufacturing integrates advanced technologies such as the Internet of Things (IoT), big data analytics, and artificial intelligence (AI) to enhance efficiencies, improve product quality, and gain a competitive advantage. Leveraging edge AI enables real-time decision-making and process optimization directly at the edge of the manufacturing network.Edge AI refers to the deployment of artificial intelligence (AI) algorithms on edge devices such as sensors, cameras, and industrial controllers, rather than relying solely on cloud-based systems. This approach reduces latency and enhances data privacy, but also helps manufacturers harness the power of AI at the source of data generation.&nbsp;<a href="https://www.fortunebusinessinsights.com/smart-manufacturing-market-103594" target="_blank">Fortune Business Insights</a> predicts that the global smart manufacturing market will grow to $658.41 billion by 2028. The market size is projected to increase from $310.92 billion in 2023 to $754.1 billion by 2030. Emerging technologies such as artificial intelligence, cloud computing, big data, and machine learning are expected to drive this growth.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzI3MDc0NTczOTI4LVNtYXJ0IE1hbnVmYWN0dXJpbmcgTWFya2V0IFNoYXJlLnBuZyIsImVkaXRzIjp7InJlc2l6ZSI6eyJ3aWR0aCI6OTUwLCJmaXQiOiJjb3ZlciJ9fX0=" style="width: 100%px;" class="fr-fic fr-dib"><h3 id="isPasted">Key Technologies Driving Smart Manufacturing Adoption</h3>According to Rockwell Automation’s <a href="https://www.rockwellautomation.com/en-us/capabilities/digital-transformation/state-of-smart-manufacturing.html" target="_blank">9th Annual State of Smart Manufacturing report</a>, manufacturing technology investment was up 30%. AI use cases are delivering outcomes that drive ROI, with AI/ML delivering more significant business outcomes than any other smart manufacturing technology.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzI3MDc0NTg2NDk3LVNtYXJ0IE1hbnVmYWN0dXJpbmcgQWRvcHRpb24ucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 100%px;" class="fr-fic fr-dib">Industrial Internet of Things (IoT) This is the backbone of smart manufacturing, facilitating seamless connectivity and data exchange between machines, sensors, and systems across the entire production life cycle.Edge computing and edge AI Computational power is brought closer to the source of data generation, enabling real-time processing and analysis of data. This allows smart factories to make intelligent decisions and take immediate actions.Advanced robotics and automation Smart factories are increasingly dependent on advanced robotics and automation technologies to streamline processes, improve accuracy, and enhance productivity.3D printing Also known as <a href="https://www.twi-global.com/technical-knowledge/faqs/what-is-additive-manufacturing" target="_blank">Additive Manufacturing</a>, 3D printing has revolutionized how products are designed and manufactured. Smart factories are leveraging 3D printing technologies to produce complex and customized components on-demand, reducing lead times, minimizing waste, and enabling greater design flexibility.Cloud computing and big data analytics They play a crucial role in smart manufacturing by enabling the storage, processing and analysis of vast amounts of data generated by IoT devices and other sources. By utilizing cloud-based platforms and advanced analytics tools, manufacturers can gain insights into production processes, identify bottlenecks, and optimize operations.<a href="https://form.jotform.com/242533284499365" target="_blank" rel="noopener noreferrer"><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1729515795640-5+Rising+Trends+for+AI+Adoption+in+Manufacturing+%283%29.png" style="width: 100%px;" class="fr-fic fr-dib"></a><h3>The Impact: Applications and Benefits of Edge AI in Smart Manufacturing</h3>Predictive maintenance Manufacturers continuously monitor equipment performance by analyzing data from sensors and IoT devices in real time. Edge AI algorithms enable the <a href="https://www.edgeimpulse.com/blog/announcing-visual-anomaly-detection/" target="_blank">detection of anomalies</a>, <a href="https://www.edgeimpulse.com/blog/pump-up-the-predictions/" target="_blank">potential failures</a>, and recommend preemptive maintenance actions, helping to minimize downtime and maximize asset utilization.&nbsp;Quality control With edge AI for real-time inspection and defect detection, manufacturers can automate the inspection process by deploying computer vision models in edge devices, enabling real-time detection of defects, anomalies, or non-conformities in products. Edge Impulse’s <a href="https://www.edgeimpulse.com/blog/announcing-fomo-faster-objects-more-objects/" target="_blank">FOMO</a> is a groundbreaking algorithm that brings real-time object detection, tracking and counting to microcontrollers for the first time.&nbsp;<a href="https://www.nvidia.com/en-gb/deep-learning-ai/solutions/ai-at-the-edge/" target="_blank">BMW</a> utilizes edge AI by placing inspection cameras on the factory floor, giving them a 360-degree view of their assembly line. Process optimization By deploying AI models on the edge, manufacturers can continuously monitor and analyze various aspects of their manufacturing processes such as resource utilization, energy consumption, and production bottlenecks, empowering them to make data-driven decisions and optimize resource allocation in real-time.Energy optimization Because of its ability to analyze real-time data from various sources, edge AI algorithms can identify inefficiencies and recommend actions to optimize energy usage, leading to significant cost savings and reduced environmental footprint.Flexibility and agility Edge AI enables the rapid reconfiguration and adaptation of production lines to accommodate changing customer demands, new product introductions, or shifts in market conditions. By processing data locally and making real-time decisions, edge AI empowers manufacturers to respond swiftly to dynamic market forces, providing a competitive edge.<h3>Manufacturing Trends and Challenges Shaping the Future</h3>According to Deloitte’s <a href="https://www2.deloitte.com/us/en/insights/industry/manufacturing/manufacturing-industry-outlook.html" target="_blank">2024 Manufacturing Industry Outlook</a> report, manufacturers should continue to embrace digital transformation to contribute to a competitive, resilient future, and to help tackle ongoing manufacturing challenges. Some of the key trends identified in the report include:Navigating the talent shortage Labor market tightness is expected to continue into 2024. In a survey conducted by the National Association of Manufacturers (NAM), more than 75% of surveyed manufacturing executives feel that attracting and retaining a quality workforce is their <a href="https://www.nam.org/wp-content/uploads/2023/03/Manufacturers_Outlook_Survey_Q2_June_2023.pdf" target="_blank" data-lf-fd-inspected-kn9eq4roawkarlvp="true">primary business challenge</a>.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzI3MDc0NzI3NTczLU1hbnVmYWN0dXJpbmcgc2tpbGxzLmpwZyIsImVkaXRzIjp7InJlc2l6ZSI6eyJ3aWR0aCI6OTUwLCJmaXQiOiJjb3ZlciJ9fX0=" style="width: 100%px;" class="fr-fic fr-dib">Image: A graph forecasting the GDP loss of the manufacturing sector by 2028In the US, the gap is predicted to reach 2.1 million roles by 2030, says <a href="https://industrytoday.com/skills-gap-a-tipping-point-for-manufacturings-future/" target="_blank">Industry Today</a>. In fact, manufacturers cite a deficit of talent and skills as their toughest challenge in scaling AI use cases. The closer use cases get to production, the harder the deficit bites, according to <a href="https://www.technologyreview.com/2024/04/09/1090880/taking-ai-to-the-next-level-in-manufacturing/" target="_blank">MIT Technology Review’s</a> Taking AI to the Next Level in Manufacturing&nbsp;report.Supply chain resilience and agility The COVID pandemic underscored the vulnerabilities of traditional supply chain models and the importance of resilience and agility. Now and in the future, manufacturers will need to focus on diversifying their supply chains, implement real-time visibility, and explore nearshoring and reshoring strategies to mitigate risk and maintain business continuity.&nbsp;Smart factories (powered by edge AI) Smart factories are rapidly becoming the new standard in the manufacturing sector’s digital transformation. This latest innovation leverages advanced technologies such as the Internet of Things (IoT), artificial intelligence (AI), robotics, and big data analytics to create interconnected systems that stream-line production, minimize downtime, and improve product quality.&nbsp;<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzI3MDc0NzQ5MTc4LVNtYXJ0IEZhY3RvcmllcyBpbnRpdGlhdGl2ZS5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 100%px;" class="fr-fic fr-dib">Sustainable and circular manufacturing With growing concerns over climate change, sustainability has become a top priority for manufacturers. We can expect to see greater emphasis on circular manufacturing practices, which involve designing products for longevity, reuse, and recycling. Additionally, the adoption of renewable energy sources and energy-efficient technologies will become more prevalent.<h3>Edge AI Platforms and Tools for Smart Manufacturing (Hardware and Software)</h3>To effectively implement edge AI solutions, smart manufacturing requires a variety of hardware, software and edge devices specifically designed for edge computing and AI workloads.&nbsp;Edge AI hardware and chipsetsAI-accelerated edge devices Devices like the NVIDIA Jetson series, Google Coral, and Intel NCS are built for edge AI workloads, featuring powerful AI processors and accelerators.Edge AI chipsets Companies like Qualcomm, AMD and Arm are developing specialized chipsets optimized for low-power, high-performance edge computing.&nbsp;Edge AI software platformsEdge Impulse Edge Impulse helps developers and machine learning engineers build, test, and deploy edge AI algorithms on edge devices, covering the entire workflow from data collection to model optimization and production monitoring.Google Cloud IoT Edge Google’s edge computing platform for deploying and managing AI models on edge devices. Microsoft Azure IoT Edge Microsoft’s solution for cloud-to-edge AI, enabling deployment of AI models on IoT devices.OpenSource frameworks Platforms like TensorFlow Lite and PyTorch Mobile enable efficient deployment of AI models on edge devices.<h3>From Sensors to Gateways: The Infrastructure Powering Edge AI in Manufacturing</h3>Smart cameras AI-enabled cameras are designed for vision inspection and defect detection.Industrial IoT sensors Connected sensors equipped with edge computing capabilities.Industrial edge gateways Such devices are typically deployed on the plant floor, close to manufacturing devices and sensors at the edge. The primary goal is to ingest the data produced on the plant floor and forward it upstream for further processing.&nbsp;These tools and platforms provide manufacturers with the necessary infrastructure to deploy edge AI solutions tailored to their specific needs, whether it’s for predictive maintenance, quality control, process optimization, or collaborative robotics.<h3>Factors to Consider When Choosing an Edge AI Platform</h3>When selecting an edge AI platform, manufacturers should consider factors such as performance requirements, integration with existing systems, scalability and security, and vendor support. Additionally, many manufacturers opt for hybrid solutions, combining on-premises edge computing with cloud-based AI services for more complex workloads or centralized model training and management.By leveraging the right combination of edge AI hardware, software, edge devices, manufacturers can effectively harness the power of AI at the edge, enabling real-time decision making, optimizing processes, and driving the digital transformation of their operations.<h3>AI in Manufacturing Use Cases in Action</h3>When it comes to edge <a href="https://www.birlasoft.com/articles/17-use-cases-of-ai-in-manufacturing" target="_blank">AI use cases</a>, the possibilities are endless. From autonomous vehicles to smart cities, retail, healthcare, and beyond, the number of use cases will continue to expand.&nbsp;<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzI3MDc0NzYxNzIxLVNjcmVlbnNob3QgMjAyNC0wNS0yOCBhdCAxLjAxLjQ04oCvUE0ucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 100%px;" class="fr-fic fr-dib">Pallet tracking and inventory managementMany industrial companies rely on tracking and managing the inventory of their products accurately. But such processes can be manual, prone to human error, and don’t offer real-time visibility into inventory. In this <a href="https://edgeimpulse.com/case-studies/industrial-asset-tracking-with-nvidia-omniverse-and-tao" target="_blank">case study</a>, learn how pallet counting, a key part of global shipping and logistics, can be automated to reduce inefficiencies and lost value.<h3>Looking Ahead</h3>The manufacturing sector will continue to transform with innovative technologies such as edge AI leading the way to the future they envision. A future defined by profitability, sustainability, agility, and efficiency. While there is much to be excited about, headwinds and challenges such as talent shortages, cybersecurity, and supply chain resilience must be addressed so that manufacturers can continue to thrive in this brave new digital landscape.<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><h3 id="isPasted">&nbsp;</h3>

Smart manufacturing integrates advanced technologies such as the Internet of Things (IoT), big data analytics, and artificial intelligence (AI) to enhance efficiencies, improve product quality, and gain a competitive advantage.

Revolutionizing Smart Manufacturing with Edge AI

When we think of&nbsp;fleet&nbsp;management&nbsp;our&nbsp;thoughts tend to turn towards the major transport&nbsp;and&nbsp;logistics&nbsp;companies,&nbsp;and the enormous fleets of vehicles they&nbsp;must&nbsp;keep&nbsp;a track&nbsp;of&nbsp;across the supply chain.&nbsp;But even the world’s biggest trucking company, United Parcel Service (UPS),&nbsp;that&nbsp;operates&nbsp;more than 340,000 vehicles&nbsp;across some 200&nbsp;countries[1],&nbsp;has a&nbsp;miniscule&nbsp;‘fleet’&nbsp;compared with&nbsp;the&nbsp;17&nbsp;billion[2]&nbsp;or so connected IoT devices that are currently employed in countless different industries, including in UPS’ own trucks.&nbsp;Billions of these devices are also deployed in fleets by the companies that service them, in practically limitless applications. It might be vending machines, medical devices, EV charging stations, or any of the smart sensors, actuators or monitoring devices installed in smart cities around the world. But deployment is just the first step, these fleets of IoT devices can’t just be launched into the wild and then forgotten about. Any IoT device fleet must be managed throughout its lifecycle to ensure effective and secure operation as well as adequate maintenance and updates, and to achieve that they require robust, stable IoT device management from the Cloud. &nbsp;&nbsp;<h2>IoT device&nbsp;data&nbsp;management&nbsp;and security</h2>Throughout the lifecycle of an IoT device—from provisioning to decommissioning—various management tasks are essential, and chief among them is data management. A fleet of IoT devices are essentially an army of data collectors, and while local processing can be efficient, it has limited scope. Sending data to the Cloud enables that data to be analyzed at scale and in near real time, driving value to the IoT device fleet owner.&nbsp;<a href="https://www.nordicsemi.com/Products/Cloud-services/Device-Management" target="_blank">Cloud-based device management</a>&nbsp;also ensures the data continues to flow without any problems.&nbsp;&nbsp;The second reason IoT devices need Cloud device management is for security. On average, every week 54 percent of organizations suffer from attempted cyber-attacks targeting IoT devices[3]. It’s an alarming statistic and highlights the need for vigilance to protect devices and data from unauthorized access and attacks. Regular firmware updates make it easier to keep devices secure, bug-free, up-to-date with new features, and capable of adapting to evolving requirements throughout their lifecycle. For the majority of IoT devices this can’t be performed via physical access or manual intervention, so the ability to execute firmware over-the-air (FOTA) updates from the Cloud is essential.&nbsp;&nbsp;<h2>Performance, scalability,&nbsp;and compliance</h2>In addition to data management and security, Cloud-based IoT device management can assist with performance, scalability, and compliance requirements. Identifying and addressing performance issues remotely and as quickly as possible not only ensures devices are operating at their full potential, but it can also greatly reduce the cost of ongoing maintenance. And as the number of IoT devices grows, deploying and managing large fleets can become a significant challenge. Certain operations for masses of devices should always be done simultaneously and a Cloud device management platform simplifies this process across different devices and applications.&nbsp;&nbsp;Finally, IoT device operators must ensure all ongoing regulatory and compliance requirements have been fulfilled, and a Cloud device management platform can ensure compliance by enforcing policies, maintaining audit trails, and facilitating reporting and documentation as may be required.&nbsp;&nbsp;<h2>One-stop solution for the IoT</h2>This growing demand for a flexible and scalable Cloud device management platform was behind the expansion of Nordic Semiconductor’s&nbsp;<a href="https://www.nordicsemi.com/Products/Cloud-services" target="_blank">Cloud Services</a>&nbsp;offering earlier this year to include&nbsp;<a href="https://www.nordicsemi.com/Products/Cloud-services/Device-Management?lang=en#infotabs" target="_blank">nRF Cloud Device Management</a>&nbsp;alongside the existing&nbsp;<a href="https://www.nordicsemi.com/Products/Cloud-services/Location-Services?lang=en#infotabs" target="_blank">location</a>&nbsp;and&nbsp;<a href="https://www.nordicsemi.com/Products/Cloud-services/Security-Services?lang=en#infotabs" target="_blank">security</a>&nbsp;services provided in the&nbsp;<a href="https://nrfcloud.com/?__hstc=8439722.f1ebab9b9306be644289de1432e134ad.1698403132381.1725606365510.1726203396952.45&__hssc=8439722.2.1726203396952&__hsfp=1429543644#/" target="_blank">nRF Cloud</a>&nbsp;suite. The launch marks the first time IoT developers and businesses have had a one-stop solution (hardware, software, and Cloud services) for the deployment and management of IoT devices at scale.&nbsp;nRF Cloud Device Management provides tools for onboarding, configuration, monitoring, and FOTA updates, as well as for comprehensive management of IoT device fleets across their lifecycle. The tools also facilitate sensor data collection services and enable technicians to ensure that fleets remain up-to-date and operate at peak efficiency. Together with the complementary location and security services, it promises developers the perfect quartet of IoT deployment requirements – reduced time-to-market, scalability, future proofing, and cost savings.<hr>References:&nbsp; 1. <a href="https://www.fleetequipmentmag.com/fleet-profile-ups-shares-results-of-strong-investment-in-alternative-fuel/" target="_blank">UPS shares results of strong investment in alternative fuel</a>.&nbsp;Fleet Equipment Magazine, January 2024 2. <a href="https://transformainsights.com/research/reports/global-iot-forecast-report-2023-2033" target="_blank">Global IoT Forecast Report, 2022-2033</a>.&nbsp;Transforma Insights, May 2024 3. <a href="https://blog.checkpoint.com/security/the-tipping-point-exploring-the-surge-in-iot-cyberattacks-plaguing-the-education-sector/" target="_blank">The Tipping Point: Exploring the Surge in IoT Cyberattacks Globally</a>. Check Point Research, April 2023&nbsp;

When we think of fleet management our thoughts tend to turn towards the major transport and logistics companies, and the enormous fleets of vehicles they must keep a track of across the supply chain.

Managing IoT Devices from the Cloud

Curious about Data Warehouse, Data Lake, and Data Lakehouse? This article breaks down their key differences and how they impact decisions on managing big data. Learn how these solutions shape industries and drive smarter data strategies.

Data Warehouse Vs Data Lake Vs Data Lakehouse: Navigating the Modern Data Storage Landscape

EPFL researchers in computer and communication sciences are hacking and fixing Android phones before malicious hackers do. They uncovered 31 critical security flaws in the Android system, explored its risks and developed methods to mitigate some of the key ones through better testing and broader mitigations.“Vulnerabilities in smart devices are the Achilles heel that can compromise the most critical aspects of a mobile device,” says Mathias Payer who leads EPFL’s <a href="https://hexhive.epfl.ch/" target="_blank">HexHive Laboratory</a> which conducts research in cyber security. “The main risk is that hackers can get a foothold in your system and gain lifelong access to your data as long as you have the same phone. Your phone is no longer secure.”<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1724286999382-ce540686.jpg" style="width: 100%px;" class="fr-fic fr-dib">Mathias Payer, head of EPFL’s HexHive Laboratory - - 2024 EPFL/Murielle Gerber - CC-BY-SA 4.0The diverse critical security flaws revealed by the researchers could have been exploited to steal personal information like fingerprints, face data, along with other sensitive data stored on one’s phone like credit card or social security information.“We studied the Android system because of the open nature of its platform, but similar security flaws are likely present in the iPhone ecosystem as well. We see much less public security research on iPhones due to Apple’s closed approach which forces researchers to first reverse engineer essential information that is publicly available on Android,” explains Payer.Marcel Busch, a postdoc in the HexHive Laboratory with Payer, spearheaded the efforts into privileged layers of Android together with the PhD students Philipp Mao and Christian Lindenmeier that resulted in three publications presented at this year’s <a href="https://www.usenix.org/conference/usenixsecurity24" target="_blank" rel="noopener">Usenix Security Symposium</a>, one of the world’s four top tier cybersecurity venues. In their work, they show exactly how these security flaws manifest themselves and which layers of the Android system’s architecture are affected.<h2>The details of the Android security flaws over three layers</h2>The Android system essentially processes information via three layers of code (iPhone’s iOS follows a similar architecture.)The first layer is the secure monitor, it’s the code that processes switches to and from the world of encrypted data known as the secure world. The second layer is divided into two parts, the secure world where sensitive data is encrypted, and the normal world built on a Linux kernel. The third layer builds on top of the second layer and contains all the apps. Day-to-day apps, like the photo app or messaging app, in the normal world talk to secure apps called Trusted Applications (TA) such as the key master app which manages cryptographic keys or the biometric information management app that contain sensitive data about the user running in the secure world.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1724287024267-3bf4537a.jpg" style="width: 100%px;" class="fr-fic fr-dib">2024 EPFL/HexHive Laboratory - CC-BY-SA 4.0<h2 id="isPasted">Numerous defects and vulnerabilities discovered</h2>The EPFL team discovered security flaws across all three layers of the Android system. The researchers developed a program (called <a href="https://github.com/HexHive/EL3XIR" target="_blank" rel="noopener">EL3XIR</a>) that essentially throws unexpected inputs at the target code to reveal software defects and vulnerabilities, a technique called fuzzing. EL3XIR revealed 34 bugs in the most fundamental and most privileged layer of Android security, the secure monitor level, of which 17 were classified as security critical (the most severe risk level).The researchers also revealed a confusion in how the Android system communicates with trusted applications. The confusion arises when information from trusted applications are mislabeled when processed between layers. In particular, the complex and critical interaction between accessible day-to-day apps and trusted applications that has to first go down through the secure monitor, and then back up through the secure world and into the trusted applications, is affected by this issue. Across 15,000 trusted applications that the team analyzed, the researchers discovered 14 new critical security flaws, uncovered 10 silently fixed bugs that vendors patched without notifying users and confirmed 9 known bugs.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1724287049262-c3e4c300.jpg" style="width: 100%px;" class="fr-fic fr-dib">Mathias Payer, Marcel Busch and Philipp Mao from the HexHive Laboratory - 2024 EPFL/Murielle Gerber - CC-BY-SA 4.0They also discovered that, if vendors did not update the Android system properly with secure patches, then hackers could force a downgrade to previous vulnerable versions of trusted applications and retrieve sensitive information, compromising the entire Android ecosystem throughout the three layered architecture. The researchers scanned over 35,000 trusted applications deployed across numerous phone manufacturers.“Android is a complex ecosystem with many different vendors and devices. Patching security vulnerabilities is complex,” says Mao, PhD candidate with the HexHive. “We followed industry standards by responsibly disclosing all our findings to the affected vendors and gave them 90 days to develop patches for their systems – which they did – before publishing any details. The insights from our findings and our automated tooling will support securing future systems.”What’s the bottom line for the consumers? Keep your system and apps up-to-date by installing updates whenever they become available, download apps only through trusted app stores and buy a device from a manufacturer that guarantees long update cycles. Busch observes that “for some of the manufacturers we studied, time-to-market is the key metric which doesn't leave much room for the diligence required for building secure systems.”<hr>ReferencesSpill the TeA: <a href="https://www.usenix.org/conference/usenixsecurity24/presentation/busch-tea" target="_blank" rel="noopener">https://www.usenix.org/conference/usenixsecurity24/presentation/busch-tea</a> EL3XIR: <a href="https://www.usenix.org/conference/usenixsecurity24/presentation/lindenmeier" target="_blank" rel="noopener">https://www.usenix.org/conference/usenixsecurity24/presentation/lindenmeier</a> Global confusion: <a href="https://www.usenix.org/conference/usenixsecurity24/presentation/busch-globalconfusion" target="_blank" rel="noopener">https://www.usenix.org/conference/usenixsecurity24/presentation/busch-globalconfusion</a>

EPFL researchers have found numerous security flaws in Android’s most privileged components © iStock

Are you concerned about hackers stealing your fingerprint and face data for accessing your smartphone? EPFL researchers have found numerous security flaws in Android’s most privileged components before hackers do and give advice to users on how to reduce risks.

Finding security flaws in Android ahead of malicious hackers

Supplementary information related to <a href="https://article.murata.com/en-eu/article/multiplexing-and-multiple-access-1" target="_blank">FDMA, TDMA, and CDMA Multiple Access (1)</a> is presented below.<h2>&lt; Column &gt; How Multiplexing Works: FDM, TDM, and CDM</h2>A number of types of multiplexing are used for wireless communication, and the following are some of the most common:• Frequency-division multiplexing (FDM) • Time-division multiplexing (TDM) • Code-division multiplexing (CDM)Figures 7-1, 7-2, and 7-3 are conceptual diagrams of these multiplexing techniques, with a focus on the transmission path portions enclosed in red dashed lines.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1722839179083-img0012-en.png" style="width: 100%px;" class="fr-fic fr-dib">Figure 7-1: Conceptual Diagram of Frequency-Division Multiplexing (FDM)<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1722839190979-img0013-en.png" style="width: 100%px;" class="fr-fic fr-dib">Figure 7-2: Conceptual Diagram of Time-Division Multiplexing (TDM)<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1722839204356-img0014-en.png" style="width: 100%px;" class="fr-fic fr-dib">Figure 7-3: Conceptual Diagram of Code-Division Multiplexing (CDM)<h3 id="isPasted">Frequency-Division Multiplexing (FDM)</h3>Frequency-division multiplexing (FDM) is a multiplexing technology that assigns multiple data streams to different frequency bands, and transmits the frequency bands as channels simultaneously via a single transmission path.Figure 7-1 shows a general conceptual diagram of FDM. On the sending side, the frequency bands assigned to the multiple data streams are treated as channels, which are sent via a single transmission path using multiplexing. On the receiving side, the transmitted data streams are extracted by devices capable of selecting specific frequency bands. An example of the use of FDM for wireless communication is terrestrial digital television broadcasting in Japan. There is a single transmission path, the air, and signals containing image and audio data are broadcast from multiple television stations on different frequencies. On the receiving side, the user can view TV shows of their choice by selecting specific frequencies. FDM is also used for analog radio broadcasting.<h3>Time-Division Multiplexing (TDM)</h3>Time-division multiplexing (TDM) is a multiplexing technology that divides up multiple data streams into time intervals assigned as channels, and transmits them on the same frequency via a single transmission path.Figure 7-2 shows a general conceptual diagram of TDM. On the sending side, multiple data streams from a device are divided into time slots and assigned to channels, which are sent via a single transmission path using multiplexing. On the receiving side, by dividing up the channels using the same timing as the sending, the transmitted data can be received by multiple devices. An example of the use of TDM for wireless communication is television broadcasting in Europe and North America.<h3>Code-Division Multiplexing (CDM)</h3>Code-division multiplexing (CDM) is a multiplexing technology that mixes with the data unique identifier codes for each device, combines the signals in a single frequency band, and then transmits the combined signals simultaneously via a single transmission path.Figure 7-3 shows how CDM works. The data is mixed (modulated) with periodic identifier codes that have a duration shorter than that of one bit of data to generate a modulated signal.*8 The multiple modulated signals from each device are added together as is, multiplexing them, and the multiplexed signals are sent via a single transmission path. On the receiving side, each device mixes in with the multiplexed signals the identifier codes used for modulation to restore (demodulate) the data and extract the transmitted data.&nbsp; There are no examples of the use of CDM as a standard for wireless communication, but adoption of the related multiple access scheme called code-division multiple access (CDMA) is widespread.*8 During transmission, the frequency bandwidth of the modulated signal ends up being wider than that of the original data stream (original signal). This characteristic provides advantages for communication applications, such as greater tolerance for external noise impinging on the transmission path.<h2>&lt; Column &gt; Explanation of the Terms FDD-LTE and TD-LTE Found in Smartphone Technical Specifications</h2>Sending and receiving take place simultaneously during mobile communications on smartphones or flip phones and phone calls on landline telephones, with a single channel being used for bidirectional transmission of audio data. Such bidirectional transmission can also be thought of as a type of multiplexing technology.Terms such as “FDD-LTE” and “TD-LTE” (TDD-LTE is typically shortened to TD-LTE) often appear in the technical specifications of products such as smartphones. FDD stands for “frequency-division duplex” and TDD stands for “time-division duplex,”*9 both of which refer to bidirectional transmission.&nbsp; Long-term evolution (LTE), on the other hand, is the telecommunications standard of the 4th generation mobile communication system (4G), and this also includes multiple access (OFDMA in the case of 4G). Therefore, “FDD-LTE” indicates both bidirectional transmission and the multiple access scheme used.&nbsp; It is somewhat complicated, but bidirectional transmission is not equivalent to multiplexing, so it is typical when writing about multiple access, which utilizes both bidirectional transmission and multiplexing, to mention the two separately.*9 FDD stands for “frequency-division duplex,” in which different frequencies are assigned to downloading (receiving of data by the device) and uploading (sending of data by the device) in order to implement bidirectional transmission (Figure 8-1). On the other hand, TDD stands for “time-division duplex,” in which the same frequency band is used for both downloading and uploading, the audio data is split up along the time axis, and sending and receiving of voice data alternates at very short intervals (on the order of milliseconds in the case of voice calls) in order to implement bidirectional transmission (Figure 8-2). Thus, strictly speaking, TDD does not provide simultaneous sending and receiving, although this results in no unnatural audible effects in the case of voice calls. Bidirectional transmission is explained in more detail at <a href="https://article.murata.com/en-eu/article/basics-of-wireless-communication-2" target="_blank">Basic Knowledge of Wireless Communication: Wireless Mechanism (2)</a>.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1722839230983-img0015-en.png" style="width: 100%px;" class="fr-fic fr-dib">Figure 8-1: Conceptual Diagram of Frequency-Division Duplex (FDD)<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1722839242889-img0016-en.png" style="width: 100%px;" class="fr-fic fr-dib">Figure 8-2: Conceptual Diagram of Time-Division Duplex (TDD)

Supplementary information related to FDMA, TDMA, and CDMA Multiple Access (1) is presented below.

FDMA, TDMA, and CDMA Multiple Access: Effective Utilization of Signals (Bandwidth) in Wireless Communication (2)

<h2 id="isPasted">Essential Technologies that Enable Effective Utilization of Signal Bandwidth for Wireless Communication</h2>Everyday our smartphones transmit and receive audio, images, videos and other data. While many people are using the same signal band at the same time, transferring huge amounts of data, we have come to think this is perfectly normal. This is possible thanks to technologies that prevent problems when sending and receiving different types of data, such as the overlapping of data or interference from noise. This article uses graphical illustrations to describe two such technologies aimed at enabling efficient utilization of frequency bandwidth (or bandwidth for short),*1 which is to say the efficient utilization of radio signals:&nbsp; • “multiplexing,” which allows multiple data streams to be transferred at the same time, and&nbsp; • “multiple access,” which makes use of multiplexing technology to allow simultaneous connections with multiple users.Note that multiple access (also called “multiaccess”) is an important technique contributing to the 5th Generation Mobile Communication System (5G), which aims to support very large numbers of simultaneous connections (1 million connections per square kilometer), including connections to “things” such as home appliances and IoT devices.*1 The wider the frequency bandwidth (bandwidth) used for wireless communication, the higher the data communication speed (transmission speed). We plan to explain the relationship between frequency bandwidth and transmission speed in another article.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzIyMjIwMjMyMjgxLWltZzAwMDEuanBnIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 100%px;" class="fr-fic fr-dib">Multiple users (devices) using mobile communications at the same time (illustrative photo)<h2 id="isPasted">Transmission of Multiple Data Streams by Wireless Communication</h2><h3>Multiplexing: Effective Utilization of a Single Transmission Path (Air)</h3>The basic model of a communication system, whether using wired or wireless communication,*2 can be represented as shown in Figure 1 (<a href="https://article.murata.com/en-eu/article/basics-of-wireless-communication-1" target="_blank">Basic Knowledge of Wireless Communication: Wireless Mechanism (1)</a>). In this model, the sending side, transmission path, and receiving side are conceived of as one unit, and the stream of data transmitted at a particular time is conceived of as one unit. (We will refer to this as “simple communication” for the time being.) Now, if the sender could send multiple streams of data to the receiving side simultaneously, the efficiency of data sending and receiving would increase. This is what led to the idea of transmitting multiple streams of data simultaneously over a single transmission path. This technology is called multiplexing.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzIyMjIwMjUzMjE4LWltZzAwMDItZW4ucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 100%px;" class="fr-fic fr-dib">Figure 1: Configuration of Basic Model for Communication Systems*2 Generally speaking, the transmission path of a communication system is either cables or air, and such systems are classified as either wired or wireless communication based on the transmission path used for telecommunication.<h3>Multiple Access: Implementing Wireless Communication among Multiple Users without Interference</h3>If we think of multiplexing as sending multiple streams of data at the same time, superimposed on each other, we can say that for multiplexing the elements of sending side and receiving side do not exist. In the case of wireless communication, the element of “transceiving” (sending and receiving combined) can be added to multiplexing to allow multiple users to share a single transmission path to transmit multiple streams of data at the same time. This is called multiple access.*3Table 1 shows the relationship between multiplexing and multiple access. To enable comparison with multiple access, we will say that multiplexing uses one sending side and one receiving side. Also, since multiplexing has multiple channels*4 and multiple access also has multiple channels, we can say that multiple access is based on multiplexing.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzIyMjIwMjc4OTEwLWltZzAwMDMtZW4ucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 100%px;" class="fr-fic fr-dib">Table 1: Relationship between Multiplexing and Multiple Access*3 In communication-related books and documents, as well as website content, multiplexing and multiple access are sometimes referred to without distinction, depending on what is being described. In the present article, multiple access is described as a technique that makes use of multiplexing to allow multiple users to share a transmission path to send and receive data.*4 Channel: As used here, this can be thought of simply as a route (communication path) along which a radio signal waveform carrying a single data stream travels. The term channel is used in this sense to refer to a signal line. In the case of wireless communication, one can describe a case in which 10 different signal waveforms are transmitted (multiplex transmission) as a communication path with 10 channels, for example.In actual wireless communication, rather than cases where a transmission path is being shared to transmit data from one user to another user, or from one user to multiple other users, cases where multiple users of a mobile communication system (Figure 2) or satellite communication system (Figure 3) share a transmission path are more common. Therefore, the discussion that follows focuses on the workings of multiple access. Multiplexing is explained in <a href="https://article.murata.com/en-eu/article/multiplexing-and-multiple-access-2#section-61975" target="_blank">&lt; Column &gt; How Multiplexing Works: FDM, TDM, and CDM</a>.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzIyMjIwMjk5MDA4LWltZzAwMDQtZW4ucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 100%px;" class="fr-fic fr-dib">Figure 2: Conceptual Diagram of Mobile Communication System<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzIyMjIwMzE0ODE3LWltZzAwMDUtZW4ucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 100%px;" class="fr-fic fr-dib">Figure 3: Conceptual Diagram of Satellite Communication System<h2 id="isPasted">How Multiple Access Works: FDMA, TDMA, and CDMA</h2>As we mentioned in the preceding section, multiple access is a technique that enables multiple users to communicate without interference by sharing multiplexed channels. The basic types of multiple access, from the least to the most advanced, are as follows: • Frequency-division multiple access (FDMA) • Time-division multiple access (TDMA) • Code-division multiple access (CDMA) Below we explain how each of these work, using conceptual diagrams of each type of multiple access and conceptual diagrams based on Figure 3 using satellite communication as an example.*5*5 Recent wireless communication standards, such as 4G-LTE mobile communication or Wi-Fi 6/6E and Wi-Fi 7 personal communication, use a scheme called orthogonal frequency-division multiple access (OFDMA) that is based on modulation and multiplexing technology called orthogonal frequency-division multiplexing (OFDM).An explanation of OFDM is omitted here because it would require a background knowledge of complex subjects such as data multivaluation, quadrature amplitude modulation (QAM), and the orthogonal properties of data signals on frequency spectra. We plan to cover this subject in another article.<h3>Frequency-Division Multiple Access (FDMA)</h3>FDMA divides up data streams from multiple users and assigns them to channels using different frequency bands, all of which share a single transmission path (Figure 4-1). Adoption of FDMA began in the 1980s, and it was used to carry cellphone and car phone calls under the analog 1st generation mobile communication system (1G).<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzIyMjIwMzM1NzgzLWltZzAwMDYtZW4ucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 100%px;" class="fr-fic fr-dib">Figure 4-1: Conceptual Diagram of Frequency-Division Multiple Access (FDMA)Figure 4-2 shows a conceptual diagram of the use of FDMA for satellite communication. Figure 4-2 shows an arrangement based on Figure 3 in which one sending side (earth station E0) and multiple receiving sides (earth stations E1 to E3) are used to transmit multiplexed data signals. The data signal from earth station E0, consisting of data streams targeted at earth stations E1 to E3, modulated and assigned to different frequencies arranged at equally spaced intervals within the bandwidth supported by the satellite relay, is sent in a manner that ensures that the adjacent signal frequency bands do not overlap (to prevent interference). When earth stations E1 to E3 receive these signals, they are divided up according to the frequency each station is able to receive, and the data streams are extracted.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzIyMjIwMzU5NDg5LWltZzAwMDctZW4ucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 100%px;" class="fr-fic fr-dib">Figure 4-2: Example of Satellite Communication Using Frequency-Division Multiple Access (FDMA)<h3 id="isPasted">Time-Division Multiple Access (TDMA)</h3>TDMA divides up data streams from multiple users into uniform time intervals and assigns them to channels, all of which share a single transmission path (Figure 5-1). Adoption of TDMA began in the 1990s, and it was used to carry mobile phone calls (GSM, PDC, PHS, etc.) under the digital 2nd generation mobile communication system (2G).<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzIyMjIwMzc3MDE1LWltZzAwMDgtZW4ucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 100%px;" class="fr-fic fr-dib">Figure 5-1: Conceptual Diagram of Time-Division Multiple Access (TDMA)Figure 5-2 shows a conceptual diagram of the use of TDMA for satellite communication. As in the preceding section, Figure 5-2 shows an arrangement based on Figure 3 in which one sending side (earth station E0) and multiple receiving sides (earth stations E1 to E3) are used to transmit multiplexed data signals.&nbsp; The data signal is sent from earth station E0, delimited by time frames and consisting of “slots” used as channels, excluding the standard burst signal*6 embedded in each frame, and each channel is assigned one of the data streams targeted at earth stations E1 to E3. Then, at each earth station from E1 to E3 the signal is divided up in a manner synchronized with the transmission timing of earth station E0, and the data streams are extracted.*6 Note that TDMA was adopted for satellite communication in addition to FDMA from around 1985.*6 To ensure stability and prevent interference with communication, TDMA requires that the channel switching speed, etc., be synchronized between the sending side and receiving side. Earth station E0 inserts a signal called a burst at the beginning of each frame for synchronization control.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzIyMjIwNDEwNzI0LWltZzAwMDktZW4ucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 100%px;" class="fr-fic fr-dib">Figure 5-2: Example of Satellite Communication Using Time-Division Multiple Access (TDMA)<h3 id="isPasted">Code-Division Multiple Access (CDMA)</h3>CDMA generates signals in which the data streams of users are mixed with unique identifier codes, and the signals of all the users are overlaid within the same frequency band and conveyed via a single transmission path (Figure 6-1). CDMA is used to carry mobile phone calls under the 3rd generation mobile communication system (3G) introduced in the 2000s.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzIyMjIwNDI5MzM4LWltZzAwMTAtZW4ucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 100%px;" class="fr-fic fr-dib">Figure 6-1: Conceptual Diagram of Code-Division Multiple Access (CDMA)Figure 6-2 shows a conceptual diagram of the use of CDMA for satellite communication. As in the preceding section, Figure 6-2 shows an arrangement based on Figure 3 in which one sending side (earth station E0) and multiple receiving sides (earth stations E1 to E3) are used to transmit multiplexed data signals.Earth station E0 mixes the identifier codes assigned to earth stations E1 to E3 on the receiving side with the data signals targeted at each earth station, generating signals with frequency bands wider than that of the original data signals, and adds them together to create a multiplexed signal that it sends to the satellite relay. Earth stations E1 to E3 each receive the multiplexed signals from the satellite relay, mix in the earth station identifier codes to divide up the signals, and extract data only from the signal that matches their own identifier code. Note that adoption of CDMA for satellite communication began in the 1990s.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzIyMjIwNDQ5Njc0LWltZzAwMTEtZW4ucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 100%px;" class="fr-fic fr-dib">Figure 6-2: Example of Satellite Communication Using Code-Division Multiple Access (CDMA)<h2 id="isPasted">Summary</h2>As we have seen, multiple access is an important technique for wireless communication, from the aspect of enabling wireless communication among multiple users without interference. Now we would like to delve into a little more detail before concluding this article.&nbsp; Multiple access schemes have become more advanced over time, evolving from FDMA to TDMA to CDMA, but it is no exaggeration to say that the increased frequency utilization efficiency*7 accompanying these advances has been a key aspect of the development of wireless communication. For example, achieving better frequency utilization efficiency in mobile communications makes it possible to increase both the number of users and the overall volume of data that can be transmitted.&nbsp; This is touched on briefly in this article, but the multiple access scheme used by 4G, the most widely used mobile communications system at present, is orthogonal frequency-division multiple access (OFDMA), which is said to provide twice the frequency utilization efficiency of 3G using CDMA. In turn, 5G uses non-orthogonal multiple access (NOMA), which delivers even better frequency utilization efficiency than OFDMA.&nbsp; So, to repeat, multiple access, which has advanced with the aim of increasing frequency utilization efficiency, can be considered indispensable to future developments in wireless communication as well as expanded adoption.*7 Frequency utilization efficiency: As used here, this can be simply be understood as the data transmission speed (in units of bits per second (bps)/Hz) per unit of frequency bandwidth. Note that frequency utilization efficiency is sometimes referred to as spectrum efficiency or bandwidth efficiency.

Everyday our smartphones transmit and receive audio, images, videos and other data. While many people are using the same signal band at the same time, transferring huge amounts of data, we have come to think this is perfectly normal.

FDMA, TDMA, and CDMA Multiple Access: Effective Utilization of Signals (Bandwidth) in Wireless Communication

Heavy equipment attachments refer to various tools and devices that can be connected to construction machinery to enhance their functionality and adaptability for different tasks. For a single machine, like an excavator, skid steer loader, or backhoe, constructors can use different tools to perform multiple functions, which significantly reduces the need for multiple specialized machines on a job site.Construction equipment attachments often include buckets for digging and scooping materials, hydraulic hammers for breaking up concrete or rock, augers for drilling and boring holes in the ground, grapples for grabbing and moving materials or debris, forks for lifting and moving pallets or loads, blades for leveling and pushing materials like soil or snow, rippers for tearing through hard or frozen ground, and compactors for compacting soil or other materials.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1721293425771-Why-Excessive-Iterations-Slow-Your-Design-Process.webp" style="width: 100%px;" class="fr-fic fr-dib">According to <a href="https://www.gminsights.com/industry-analysis/construction-equipment-attachments-market" target="_blank" id="isPasted">Global Market Insights</a>, the construction equipment attachments market size was valued at USD 6 billion in 2022 and is poised to register a compound annual growth rate (CAGR) of over 4% between 2023 and 2032. The high demand for construction machinery attachments is linked to the increase in number and scale of construction projects.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1721293457157-Graphic-Construction-Equipment-Attachments-Sales-Market.webp" style="width: 100%px;" class="fr-fic fr-dib"><h2 id="isPasted">Compatibility Issue, Barrier to Investment</h2>Separate manufacturers for attachments are common in the heavy industry, even though construction equipment manufacturers could potentially produce these accessories themselves. The main reason resides in the expertise to build dedicated attachments to meet specific needs. These specialized attachment manufacturers can also be more agile in responding to market needs and innovations.Nevertheless, ensuring <a href="https://www.creaform3d.com/blog/metrascan-3d-helps-to-create-pipeline-machinery-improvements/" target="_blank">the right fit between attachments and equipment</a> is complex. Even with their expertise, accessory manufacturers face this significant obstacle: designing products that can be attached to any brand or model of machinery. Contractors may also hesitate to invest in attachments that may not be compatible with their existing machinery.<h2>Countless Iteration, Endless Prototyping</h2>To develop a custom attachment, designers often engage in a never-ending prototyping process with countless iterations. For instance, a manufacturer might spend weeks creating a prototype only to find that it does not fit the equipment properly. This long product development process originates from the difficult access to the machinery, the increased complexity of heavy equipment, and the lack of data collected for reverse engineering.<h3>Difficult Access to Operating Machinery</h3>Specialized attachment manufacturers generally do not own the heavy equipment. So, they must have access to the equipment to take measurements, but the machine is usually in continual operation and unavailable.Even when they are granted access, it is often parked outside, covered with dirt and dust, exposed to harsh weather, and with other heavy vehicles traveling nearby. As a result, data collection must be done quickly.<h3>More Technologically Complex Machines</h3>Heavy equipment has grown increasingly complex over the years due to the integration of advanced hydraulics, sophisticated control systems, connectivity, and automation technologies.As a result, designing attachments also becomes complex as there are more physical elements to consider. Designers must have a clear and <a href="https://www.creaform3d.com/blog/3d-scanning-helps-with-the-design-of-engine-block-mounts/" target="_blank">in-depth picture of the environment</a> to which the accessory will be attached.<h3>Lack of Accurate Data</h3>When designing an attachment, the first step is to assess the environment to which it will be juxtaposed. While designers may have access to computer-aided design (CAD) data for the equipment, it may not match the actual model in the field. This is why the design team prefers to take their own measurements to ensure compatibility.However, collecting enough accurate data can be challenging, and once back in the office, the team needs to be able to locate these measuring points to reconstruct the surface precisely. This can be challenging if they only rely on a measuring tape and pictures.<h2>Inefficient Design Process: Waste of Time and Siphoning of Money</h2>All these obstacles impact the design process. Insufficient data quantity and quality means the first prototype will not fit perfectly. Hence, new measurements will be necessary to correct it. This involves returning to the equipment’s location and checking for compatibility. A second prototype will then be produced, which will, hopefully, be compatible.The compatibility of prototypes is directly proportional to the quality and quantity of the collected data. Insufficient data risks causing other whereabouts and adjustments until a prototype with the right fit is finally produced. This time-consuming and costly process diverts resources from other profitable activities.<h2>Getting the Right Fit with One Prototype</h2>Is achieving a <a href="https://www.creaform3d.com/blog/vehicle-manufacturing-company-uses-3d-scanners-to-increase-design-quality-and-optimize-engineering-times/" target="_blank">perfect fit on the first try</a> with one prototype realistic? The answer is yes if the data is collected from the beginning with 3D scanning technology. With a 3D scanner, technicians can capture the complete 3D dimensions of surfaces in just one session, regardless of size and complexity. This large amount of data, combined with the accuracy of the measurements, allows designers to precisely reconstruct the environment to which their accessory will be attached. 3D scanning enables them to work with the correct information from the start, eliminating the need to redo measurements and iterations.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1721293471135-HandySCAN-MAX_Scanner_Fork-lift-1536x1024.webp" style="width: 100%px;" class="fr-fic fr-dib">Since a 3D scanner is easy to handle and set up, technicians can either capture the data set more quickly or <a href="https://www.creaform3d.com/en/handyscan-3d-max-series-for-large-objects" target="_blank" id="isPasted">measure a larger area</a> from different angles in the same amount of time, thus ensuring better confidence in the 3D model.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1721293519528-1721293519528.png" class="fr-fic fr-dib"><h2 id="isPasted">Reduced Design Time: Better Efficiency, More Business</h2>No longer having to travel numerous times to retake measurements and redo prototypes saves enormous design time. And, saving time in the design stage can significantly enhance the manufacturer’s efficiency, customer satisfaction, and profitability.As a result, the company can produce more attachments, meet more demands, and get more business. It can also deliver products with the right fit faster to customers, leading to higher satisfaction and possibly more contracts in the future. Finally, producing fewer prototypes means reducing waste, thereby reducing the cost per unit and leading to higher profit margins.<h2>Seamless Integration of 3D Scanning Technology</h2>Still, some attachment manufacturers may perceive 3D scanning as a complicated technology and may think that they will need a completely new 3D infrastructure to handle the 3D scanning data.

Is achieving a perfect fit on the first try with one prototype realistic? The answer is yes if the data is collected from the beginning with 3D scanning tech. With a 3D scanner, technicians can capture the complete 3D dimensions of surfaces in just one session, regardless of size and complexity.

Why Excessive Iterations Slow Your Design Process

Heavy construction equipment refers to large, heavy-duty machines used in the construction industry for different purposes. This robust machinery includes excavators for digging and trenching, bulldozers for pushing soil and sand, loaders for transporting materials, backhoes for both digging and backfilling, cranes for lifting and moving heavy materials, dump trucks for hauling and dumping materials, motor graders for leveling and grading surfaces, compactors for compressing soil, pavers for laying surfaces, drilling rigs for creating holes, and concrete mixers for mixing and transporting concrete.According to the Global Heavy Construction Equipment Market Research Report, the heavy construction equipment market was valued at USD 195.04 Bn in 2023 and is predicted to reach USD 273.94 Bn by 2031 at a compound annual growth rate (CAGR) of 4.48% during the forecast period 2024-2031.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1719564056991-Graphic-Global-Heavy-Construction-Equipment-Market-Research-Report-1.webp" style="width: 100%px;" class="fr-fic fr-dib"><h2 id="isPasted">Factors Driving the Expansion of the Heavy Construction Equipment Market</h2>As the population grows and more people move to urban areas, there is a rising need for infrastructure development, including roads, bridges, buildings, and public utilities. Many governments invest in infrastructure projects worldwide to stimulate economic growth and improve public services. These investments often involve large-scale construction projects that require heavy equipment. The ongoing need to refurbish or replace aging infrastructure also supports this market growth.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1719564075655-Building-under-construction-and-wide-dirt-ground-1536x1025.webp" style="width: 100%px;" class="fr-fic fr-dib">The integration of new technologies into heavy construction equipment, such as GPS, IoT, and automation, has increased efficiency and productivity, making these machines more attractive to construction companies. According to Volvo, construction equipment manufacturers are starting to mirror Silicon Valley tech firms, advancing in telematics, autonomous machinery, and electromobility—to name just a few.Furthermore, stricter regulations and a growing awareness of the need to reduce carbon emissions have led to a willingness to adopt renewable energy sources and electrify construction machinery, which creates new possibilities and openings in the industry. Finally, the increasing demand for resources, such as minerals, ores, and rare metals, drives the need for heavy construction equipment used in mining operations.<h2>Opportunities Arising from Market Growth</h2>The growth of the heavy construction equipment market presents significant opportunities for major players, such as Caterpillar, Liebherr AG, Terex Corporation, Volvo Group, and Komatsu, to expand their businesses and strengthen their market positions by entering emerging economies where infrastructure development is a priority.They can also capitalize on the expanding market by innovating and improving their product offerings, incorporating advanced technologies such as automation, telematics, and electrification. One thing is certain, though: the quality of their machines must remain impeccable.<h2>Quality Defects are Expensive</h2>Every heavy equipment machine is expensive, and every hour wasted on a construction site due to quality issues is costly. Heavy equipment presenting quality defects leads to higher maintenance and repair costs for customers, resulting in warranty claims and the need for manufacturers to provide additional support, which can also become very costly.<h3>Warranty Claims Eating Into Profit Margins</h3>Major heavy equipment manufacturers often measure their product quality with the number of warranty claims they receive. When resources are spent on managing warranty claims, they cannot be used for other profit-generating activities, such as new product development or new market penetration. This represents an opportunity cost that can impact the company’s overall profitability. Moreover, frequent warranty claims can harm a company’s reputation and lead to a loss of customer trust and loyalty, decreasing its sales and market share.<h3>Identifying the Root Cause Quickly and Efficiently…</h3>When a technical problem occurs, such as a vibration, the quality assurance department is called in, like firefighters, to put out the fire and identify the root cause. But this task is not always easy when you don’t know what you are looking for. Added to this challenge is the fact that heavy equipment machines are made of&nbsp;<a href="https://www.creaform3d.com/blog/do-you-have-trouble-measuring-and-analyzing-large-parts/" target="_blank">huge parts</a> with complex freeform shapes.Using manual tools, such as a measuring tape or a caliper, to find where a vibration comes from can quickly become daunting—the same observation is valid with a measuring arm that can only probe points here and there. Measuring the dimensions of a handling machine’s engine hood with a defined grid—probing one point on each corner—can take at least one working day. And despite all this work, the quality department does not have the overall picture of the part to conduct its investigation.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1719564130380-HandySCAN-MAX_scanning-constuction-truck-hood-1536x703.webp" style="width: 100%px;" class="fr-fic fr-dib"> With a scanning solution, however, quality professionals can capture the complete 3D dimensions of surfaces, regardless of size and complexity. Then, they can compare these 3D models with the CAD and quickly see where there are discrepancies. They can also check critical features, such as the position of holes and the alignment of axes. 3D scan data makes them much better equipped to identify the root cause and rapidly solve the technical issue described in the warranty claims.<h3>…to Reduce Warranty Claims</h3>Having&nbsp;<a href="https://www.creaform3d.com/blog/the-difference-between-stated-accuracy-and-accredited-accuracy/" target="_blank">quality 3D data</a> in large quantities to recreate complete 3D surfaces enables the quality assurance department to address the root causes of product defects effectively. Since resources are limited and there are only 24 hours in a day, it is important to have access to this accurate 3D data quickly to diagnose and resolve technical issues faster than they accumulate.As the fires go out, heavy equipment manufacturers will be able to shift their focus from reactive quality assurance and troubleshooting to&nbsp;<a href="https://www.creaform3d.com/blog/vehicle-manufacturing-company-uses-3d-scanners-to-increase-design-quality-and-optimize-engineering-times/" target="_blank">proactive quality control</a>. This inspection process aims to ensure that the welded parts adhere to a defined set of quality criteria before assembling them and shipping the final machines to customers.Investing in quality control inevitably results in better-quality products that are less likely to malfunction or break down. This enables heavy equipment manufacturers to&nbsp;<a href="https://www.creaform3d.com/blog/use-of-3d-scanner-increases-efficiency-and-reduces-labor-and-material-costs/" target="_blank">reduce the costs</a> associated with warranty claims, repairing or replacing products, and handling customer complaints. Higher-quality products are also more likely to meet and even exceed customer expectations.We all know that satisfied customers are more likely to become repeat buyers and refer others, thus increasing sales revenue. Having a reputation for quality can also bolster a company’s brand image, making it more appealing to customers and potentially leading to an increase in market share. A brand name synonymous with quality also allows companies to maintain higher prices and benefit from higher margins. Customers are generally willing to pay more for durable and reliable machines that perform well. In short, improving quality has a direct impact on the profit margins.<h2>Confidence in 3D Data</h2>Still, quality professionals must believe in the reliability of the measurement data rather than continually challenge and discredit their accuracy.

Investing in quality control inevitably results in better-quality products that are less likely to malfunction or break down. This enables heavy equipment manufacturers to reduce the costs associated with warranty claims, repairing or replacing products, and handling customer complaints.

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