#Machine Learning Sensor Market

[adiya60]

Virtual Sensor Market Graph: Growth, Share, Value, Size, and Insights

"Virtual Sensor Market Size And Forecast by 2029

According to Data Bridge Market Research The virtual sensor market is expected to witness market growth at a rate of 31.4% in the forecast period of 2022 to 2029. 

Virtual Sensor Market aims to expand its operations with strategic initiatives and global investments. With a strong roadmap, AI-Driven Sensor Market plans to enter new markets and increase its footprint. The expansion strategy of Software Sensor Market includes technological advancements and enhanced service models. Cloud-Based Sensor Market is committed to maintaining leadership through progressive developments. Future innovations from Virtual Sensor Market will redefine industry standards and drive business growth.

As a leader, Virtual Sensor Market sets new standards by implementing groundbreaking solutions. The contributions of Predictive Analytics Sensor Market to the industry reflect its strong commitment to excellence. By investing in sustainable practices, Virtual Sensor Market ensures long-term success. The leadership of Virtual Sensor Market inspires innovation and fosters competition within the sector. Digital Twin Sensor Market continues to reinforce its position through forward-thinking strategies and visionary growth.

Our comprehensive Virtual Sensor Market report is ready with the latest trends, growth opportunities, and strategic analysis. https://www.databridgemarketresearch.com/reports/global-virtual-sensor-market

**Segments**

- By Component: The virtual sensor market is segmented by component into solutions and services. The solutions segment is further categorized into hardware and software solutions. Hardware solutions involve the physical components used to create virtual sensors, while software solutions include the algorithms and platforms used to develop virtual sensor capabilities. On the other hand, the services segment comprises consulting, implementation, and support services essential for deploying and maintaining virtual sensor systems effectively. - By Deployment Mode: In terms of deployment mode, the market is divided into cloud-based and on-premises solutions. Cloud-based deployment offers scalability, flexibility, and cost-effectiveness by enabling virtual sensors to be hosted on third-party servers, whereas on-premises deployment provides greater control and security but may require higher initial investments and maintenance costs. - By End-User: The virtual sensor market caters to various industry verticals such as manufacturing, automotive, healthcare, aerospace and defense, and others. Each sector utilizes virtual sensors differently to monitor and analyze data for optimizing operations, enhancing performance, and ensuring safety compliance. For example, in manufacturing, virtual sensors are used for predictive maintenance and process optimization, while in healthcare, they help in remote patient monitoring and personalized treatment strategies.

**Market Players**

- Microsoft Corporation: Microsoft offers virtual sensor solutions as part of its Azure IoT platform, providing advanced analytics and AI capabilities to build and deploy virtual sensor models efficiently. - IBM Corporation: IBM's Watson IoT platform includes virtual sensor technology that enables organizations to create predictive maintenance and quality control applications using machine learning algorithms. - Siemens AG: Siemens provides virtual sensor solutions for industrial applications, leveraging its expertise in automation, digitalization, and AI to deliver real-time insights and predictive analytics for businesses. - General Electric: GE offers virtual sensor capabilities through its Predix platform, enabling enterprises to harness the power of data analytics and machine learning for optimizing asset performance and operational efficiency. - PTC Inc.: PTC's ThingWorx platform integrates virtual sensor functionalities to enable organizations to collect, analyze, and visualize sensor data in real-time, supporting better decision-making and automation processes.

For more insights into the Global Virtual Sensor Market, visit: https://www.databridgemarketresearch.com/reports/global-virtual-sensor-market The Global Virtual Sensor Market is witnessing significant growth driven by the increasing adoption of IoT technology across various industries. Virtual sensors play a crucial role in gathering, analyzing, and interpreting data from connected devices to enable predictive maintenance, process optimization, and real-time decision-making. As the demand for data-driven insights continues to rise, the virtual sensor market is poised for continued expansion, with a focus on enhancing operational efficiency, reducing downtime, and improving overall performance.

One of the key trends shaping the market is the integration of virtual sensor technology into advanced analytics and AI platforms. Market players like Microsoft, IBM, Siemens, General Electric, and PTC are at the forefront of developing innovative solutions that combine virtual sensors with machine learning algorithms to deliver actionable insights and predictive capabilities. These technologies are enabling organizations to extract valuable information from sensor data streams, leading to more efficient operations and improved business outcomes.

Moreover, the market segmentation by component (solutions and services), deployment mode (cloud-based and on-premises), and end-user verticals (manufacturing, automotive, healthcare, aerospace and defense, etc.) highlights the diverse applications of virtual sensor technology across various sectors. Manufacturers are leveraging virtual sensors for predictive maintenance and quality control, while healthcare providers are using them for remote patient monitoring and personalized treatment strategies. This broad industry adoption underscores the versatility and scalability of virtual sensor solutions in addressing different business needs and challenges.

Looking ahead, the Global Virtual Sensor Market is expected to witness continued growth as organizations invest in digital transformation initiatives and IoT-enabled solutions. The convergence of virtual sensor technology with other cutting-edge technologies such as edge computing, 5G connectivity, and advanced analytics will further drive innovation in this space, opening up new opportunities for market players to deliver value-added services and solutions to their customers.

In conclusion, the Global Virtual Sensor Market is a dynamic and evolving landscape characterized by technological advancements, industry partnerships, and growing demand for data-driven insights. With market players focusing on developing more sophisticated virtual sensor solutions and services, the future outlook for the virtual sensor market appears promising as organizations seek to harness the power of data analytics and IoT to drive operational efficiency and business growth.The Global Virtual Sensor Market is currently experiencing substantial growth due to the widespread adoption of IoT technology across various industries worldwide. Virtual sensors have become essential in collecting, analyzing, and interpreting data from interconnected devices to enable predictive maintenance, process optimization, and real-time decision-making. As organizations increasingly rely on data-driven insights to enhance operational efficiency, reduce downtime, and improve overall performance, the demand for virtual sensor solutions is on the rise.

One of the key trends shaping the market is the integration of virtual sensor technology into advanced analytics and AI platforms. Market leaders such as Microsoft, IBM, Siemens, General Electric, and PTC are driving innovation by combining virtual sensors with sophisticated machine learning algorithms to provide actionable insights and predictive capabilities. This integration allows organizations to extract valuable information from sensor data streams, leading to more efficient operations and better business outcomes.

Market segmentation by component (solutions and services), deployment mode (cloud-based and on-premises), and end-user verticals (manufacturing, automotive, healthcare, aerospace and defense, etc.) showcases the diverse applications of virtual sensor technology across different sectors. Manufacturers utilize virtual sensors for predictive maintenance and quality control, while healthcare providers leverage them for remote patient monitoring and personalized treatment strategies. The broad industry adoption underlines the versatility and scalability of virtual sensor solutions in addressing various business needs and challenges.

Looking ahead, the Global Virtual Sensor Market is expected to continue its growth trajectory as organizations invest in digital transformation initiatives and IoT-enabled solutions. The convergence of virtual sensor technology with cutting-edge technologies such as edge computing, 5G connectivity, and advanced analytics will further propel innovation, offering new opportunities for market players to deliver value-added services and solutions to their clients.

In summary, the Global Virtual Sensor Market presents a dynamic and evolving landscape characterized by technological advancements, strategic industry partnerships, and a growing appetite for data-driven insights. With market players focusing on the development of more sophisticated virtual sensor solutions and services, the future outlook for the virtual sensor market appears promising as organizations seek to harness the power of data analytics and IoT to drive operational efficiency and foster business growth in the increasingly competitive global landscape.

The market is highly fragmented, with a mix of global and regional players competing for market share. To Learn More About the Global Trends Impacting the Future of Top 10 Companies in Virtual Sensor Market :   https://www.databridgemarketresearch.com/reports/global-virtual-sensor-market/companies

Key Questions Answered by the Global Virtual Sensor Market Report:

What is the current state of the Virtual Sensor Market, and how has it evolved?

What are the key drivers behind the growth of the Virtual Sensor Market?

What challenges and barriers do businesses in the Virtual Sensor Market face?

How are technological innovations impacting the Virtual Sensor Market?

What emerging trends and opportunities should businesses be aware of in the Virtual Sensor Market?

Browse More Reports:

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[marketresearchnews24]

Smart Traction: Intelligent All-Wheel Drive Market Accelerates to $49.3 Billion by 2030

The intelligent all-wheel drive market is experiencing remarkable momentum as automotive manufacturers integrate advanced electronics and artificial intelligence into drivetrain systems to deliver superior performance, safety, and efficiency. With an estimated revenue of $29.9 billion in 2024, the market is projected to grow at an impressive compound annual growth rate (CAGR) of 8.7% from 2024 to 2030, reaching $49.3 billion by the end of the forecast period. This robust growth reflects the automotive industry's evolution toward smarter, more responsive drivetrain technologies that adapt dynamically to changing road conditions and driving scenarios.

Evolution Beyond Traditional All-Wheel Drive

Intelligent all-wheel drive systems represent a significant advancement over conventional mechanical AWD configurations, incorporating sophisticated electronic controls, multiple sensors, and predictive algorithms to optimize traction and handling in real-time. These systems continuously monitor wheel slip, steering input, throttle position, and road conditions to make instantaneous adjustments to torque distribution between front and rear axles, and increasingly between individual wheels.

Unlike traditional AWD systems that react to wheel slip after it occurs, intelligent systems use predictive algorithms and sensor data to anticipate traction needs before wheel slip begins. This proactive approach enhances vehicle stability, improves fuel efficiency, and provides superior performance across diverse driving conditions from highway cruising to off-road adventures.

Consumer Demand for Enhanced Safety and Performance

Growing consumer awareness of vehicle safety and performance capabilities is driving increased demand for intelligent AWD systems. Modern drivers expect vehicles that can confidently handle adverse weather conditions, challenging terrain, and emergency maneuvering situations. Intelligent AWD systems provide these capabilities while maintaining the fuel efficiency advantages of front-wheel drive during normal driving conditions.

The rise of active lifestyle trends and outdoor recreation activities has increased consumer interest in vehicles capable of handling diverse terrain and weather conditions. Intelligent AWD systems enable crossovers and SUVs to deliver genuine all-terrain capability without compromising on-road refinement and efficiency.

SUV and Crossover Market Expansion

The global shift toward SUVs and crossover vehicles is a primary driver of intelligent AWD market growth. These vehicle segments increasingly offer AWD as standard equipment or popular options, with intelligent systems becoming key differentiators in competitive markets. Manufacturers are positioning advanced AWD capabilities as premium features that justify higher trim levels and increased profitability.

Luxury vehicle segments are particularly driving innovation in intelligent AWD technology, with features such as individual wheel torque vectoring, terrain-specific driving modes, and integration with adaptive suspension systems. These advanced capabilities create compelling value propositions for consumers seeking both performance and versatility.

Electric Vehicle Integration Opportunities

The electrification of automotive powertrains presents unique opportunities for intelligent AWD systems. Electric vehicles can implement AWD through individual wheel motors or dual-motor configurations that provide precise torque control impossible with mechanical systems. Electric AWD systems offer instant torque delivery, regenerative braking coordination, and energy management optimization.

Hybrid vehicles benefit from intelligent AWD systems that coordinate internal combustion engines with electric motors to optimize performance and efficiency. These systems can operate in electric-only AWD mode for quiet, emissions-free driving or combine power sources for maximum performance when needed.

Advanced Sensor Technology and Data Processing

Modern intelligent AWD systems incorporate multiple sensor technologies including accelerometers, gyroscopes, wheel speed sensors, and increasingly, cameras and radar systems that monitor road conditions ahead of the vehicle. Machine learning algorithms process this sensor data to predict optimal torque distribution strategies for varying conditions.

GPS integration enables intelligent AWD systems to prepare for upcoming terrain changes, weather conditions, and road characteristics based on location data and real-time traffic information. This predictive capability allows systems to optimize performance before challenging conditions are encountered.

Manufacturer Competition and Innovation

Intense competition among automotive manufacturers is driving rapid innovation in intelligent AWD technology. Brands are developing proprietary systems with unique characteristics and branding to differentiate their vehicles in crowded markets. This competition accelerates technological advancement while providing consumers with increasingly sophisticated options.

Partnerships between automotive manufacturers and technology companies are creating new capabilities in intelligent AWD control systems. Artificial intelligence, cloud computing, and advanced materials are being integrated to create more responsive and efficient systems.

Regional Market Dynamics

Different global markets exhibit varying demand patterns for intelligent AWD systems based on climate conditions, terrain characteristics, and consumer preferences. Northern markets with harsh winter conditions show strong demand for advanced traction systems, while emerging markets focus on systems that provide value-oriented performance improvements.

Regulatory requirements for vehicle stability and safety systems in various regions influence the adoption of intelligent AWD technology. Standards for electronic stability control and traction management create baseline requirements that intelligent AWD systems can exceed.

Manufacturing and Cost Considerations

The increasing sophistication of intelligent AWD systems requires significant investment in research and development, manufacturing capabilities, and supplier relationships. However, economies of scale and advancing semiconductor technology are helping to reduce system costs while improving performance and reliability.

Modular system designs enable manufacturers to offer different levels of AWD sophistication across vehicle lineups, from basic intelligent systems in entry-level models to advanced torque-vectoring systems in performance vehicles.

[reasonsforhope]

"A 9th grader from Snellville, Georgia, has won the 3M Young Scientist Challenge, after inventing a handheld device designed to detect pesticide residues on produce.

Sirish Subash set himself apart with his AI-based sensor to win the grand prize of $25,000 cash and the prestigious title of “America’s Top Young Scientist.”

Like most inventors, Sirish was intrigued with curiosity and a simple question. His mother always insisted that he wash the fruit before eating it, and the boy wondered if the preventative action actually did any good.

He learned that 70% of produce items contain pesticide residues that are linked to possible health problems like cancer and Alzheimer’s—and washing only removes part of the contamination.

“If we could detect them, we could avoid consuming them, and reduce the risk of those health issues.”

His device, called PestiSCAND, employs spectrophotometry, which involves measuring the light that is reflected off the surface of fruits and vegetables. In his experiments he tested over 12,000 samples of apples, spinach, strawberries, and tomatoes. Different materials reflect and absorb different wavelengths of light, and PestiSCAND can look for the specific wavelengths related to the pesticide residues.

After scanning the food, PestiSCAND uses an AI machine learning model to analyze the lightwaves to determine the presence of pesticides. With its sensor and processor, the prototype achieved a detection accuracy rate of greater than 85%, meeting the project’s objectives for effectiveness and speed.

Sirish plans to continue working on the prototype with a price-point goal of just $20 per device, and hopes to get it to market by the time he starts college." [Note: That's in 4 years.]

-via Good News Network, October 27, 2024

[probablyasocialecologist]

Consider the ways oil and gas are already entwined with big tech. The foundation of the partnership between Big Tech and Big Oil is the cloud, explains Zero Cool, a software expert who went to Kazakhstan to do work for Chevron and chronicled this in Logic magazine. “For Amazon, Google, and Microsoft, as well as a few smaller cloud competitors like Oracle and IBM, winning the IT spend of the Fortune 500 is where most of the money in the public cloud market will be made”—and out of the biggest ten companies in the world by revenue, six are in the business of oil production. What are oil companies going to do with the cloud? Apparently, Chevron—which signed a seven-year cloud contract with Microsoft—generates a terabyte of data per day per sensor and has thousands of wells with these sensors. They can’t even use all that data because of the scale of computation required. “Big Tech doesn’t just supply the infrastructures that enable oil companies to crunch their data,” explains Zero Cool; they also provide analytic tools, and machine learning can help discover patterns to run their operations more efficiently. This is another reason why Big Oils need Big Tech; they have the edge when it comes to artificial intelligence/machine learning. “Why go through the effort of using clean energy to power your data centers when those same data centers are being used by companies like Chevron to produce more oil?” Zero Cool asks, also noting that one of the main reasons oil companies are interested in technology is to surveil workers.

Holly Jean Buck, Ending Fossil Fuels: Why Net Zero is Not Enough

[mostlysignssomeportents]

Solar is a market for (financial) lemons

There are only four more days left in my Kickstarter for the audiobook of The Bezzle, the sequel to Red Team Blues, narrated by @wilwheaton! You can pre-order the audiobook and ebook, DRM free, as well as the hardcover, signed or unsigned. There's also bundles with Red Team Blues in ebook, audio or paperback.

Rooftop solar is the future, but it's also a scam. It didn't have to be, but America decided that the best way to roll out distributed, resilient, clean and renewable energy was to let Wall Street run the show. They turned it into a scam, and now it's in terrible trouble. which means we are in terrible trouble.

There's a (superficial) good case for turning markets loose on the problem of financing the rollout of an entirely new kind of energy provision across a large and heterogeneous nation. As capitalism's champions (and apologists) have observed since the days of Adam Smith and David Ricardo, markets harness together the work of thousands or even millions of strangers in pursuit of a common goal, without all those people having to agree on a single approach or plan of action. Merely dangle the incentive of profit before the market's teeming participants and they will align themselves towards it, like iron filings all snapping into formation towards a magnet.

But markets have a problem: they are prone to "reward hacking." This is a term from AI research: tell your AI that you want it to do something, and it will find the fastest and most efficient way of doing it, even if that method is one that actually destroys the reason you were pursuing the goal in the first place.

https://learn.microsoft.com/en-us/security/engineering/failure-modes-in-machine-learning

For example: if you use an AI to come up with a Roomba that doesn't bang into furniture, you might tell that Roomba to avoid collisions. However, the Roomba is only designed to register collisions with its front-facing sensor. Turn the Roomba loose and it will quickly hit on the tactic of racing around the room in reverse, banging into all your furniture repeatedly, while never registering a single collision:

https://www.schneier.com/blog/archives/2021/04/when-ais-start-hacking.html

This is sometimes called the "alignment problem." High-speed, probabilistic systems that can't be fully predicted in advance can very quickly run off the rails. It's an idea that pre-dates AI, of course – think of the Sorcerer's Apprentice. But AI produces these perverse outcomes at scale…and so does capitalism.

Many sf writers have observed the odd phenomenon of corporate AI executives spinning bad sci-fi scenarios about their AIs inadvertently destroying the human race by spinning off in some kind of paperclip-maximizing reward-hack that reduces the whole planet to grey goo in order to make more paperclips. This idea is very implausible (to say the least), but the fact that so many corporate leaders are obsessed with autonomous systems reward-hacking their way into catastrophe tells us something about corporate executives, even if it has no predictive value for understanding the future of technology.

Both Ted Chiang and Charlie Stross have theorized that the source of these anxieties isn't AI – it's corporations. Corporations are these equilibrium-seeking complex machines that can't be programmed, only prompted. CEOs know that they don't actually run their companies, and it haunts them, because while they can decompose a company into all its constituent elements – capital, labor, procedures – they can't get this model-train set to go around the loop:

https://pluralistic.net/2023/03/09/autocomplete-worshippers/#the-real-ai-was-the-corporations-that-we-fought-along-the-way

Stross calls corporations "Slow AI," a pernicious artificial life-form that acts like a pedantic genie, always on the hunt for ways to destroy you while still strictly following your directions. Markets are an extremely reliable way to find the most awful alignment problems – but by the time they've surfaced them, they've also destroyed the thing you were hoping to improve with your market mechanism.

Which brings me back to solar, as practiced in America. In a long Time feature, Alana Semuels describes the waves of bankruptcies, revealed frauds, and even confiscation of homeowners' houses arising from a decade of financialized solar:

https://time.com/6565415/rooftop-solar-industry-collapse/

The problem starts with a pretty common finance puzzle: solar pays off big over its lifespan, saving the homeowner money and insulating them from price-shocks, emergency power outages, and other horrors. But solar requires a large upfront investment, which many homeowners can't afford to make. To resolve this, the finance industry extends credit to homeowners (lets them borrow money) and gets paid back out of the savings the homeowner realizes over the years to come.

But of course, this requires a lot of capital, and homeowners still might not see the wisdom of paying even some of the price of solar and taking on debt for a benefit they won't even realize until the whole debt is paid off. So the government moved in to tinker with the markets, injecting prompts into the slow AIs to see if it could coax the system into producing a faster solar rollout – say, one that didn't have to rely on waves of deadly power-outages during storms, heatwaves, fires, etc, to convince homeowners to get on board because they'd have experienced the pain of sitting through those disasters in the dark.

The government created subsidies – tax credits, direct cash, and mixes thereof – in the expectation that Wall Street would see all these credits and subsidies that everyday people were entitled to and go on the hunt for them. And they did! Armies of fast-talking sales-reps fanned out across America, ringing dooorbells and sticking fliers in mailboxes, and lying like hell about how your new solar roof was gonna work out for you.

These hustlers tricked old and vulnerable people into signing up for arrangements that saw them saddled with ballooning debt payments (after a honeymoon period at a super-low teaser rate), backstopped by liens on their houses, which meant that missing a payment could mean losing your home. They underprovisioned the solar that they installed, leaving homeowners with sky-high electrical bills on top of those debt payments.

If this sounds familiar, it's because it shares a lot of DNA with the subprime housing bubble, where fast-talking salesmen conned vulnerable people into taking out predatory mortgages with sky-high rates that kicked in after a honeymoon period, promising buyers that the rising value of housing would offset any losses from that high rate.

These fraudsters knew they were acquiring toxic assets, but it didn't matter, because they were bundling up those assets into "collateralized debt obligations" – exotic black-box "derivatives" that could be sold onto pension funds, retail investors, and other suckers.

This is likewise true of solar, where the tax-credits, subsidies and other income streams that these new solar installations offgassed were captured and turned into bonds that were sold into the financial markets, producing an insatiable demand for more rooftop solar installations, and that meant lots more fraud.

Which brings us to today, where homeowners across America are waking up to discover that their power bills have gone up thanks to their solar arrays, even as the giant, financialized solar firms that supplied them are teetering on the edge of bankruptcy, thanks to waves of defaults. Meanwhile, all those bonds that were created from solar installations are ticking timebombs, sitting on institutions' balance-sheets, waiting to go blooie once the defaults cross some unpredictable threshold.

Markets are very efficient at mobilizing capital for growth opportunities. America has a lot of rooftop solar. But 70% of that solar isn't owned by the homeowner – it's owned by a solar company, which is to say, "a finance company that happens to sell solar":

https://www.utilitydive.com/news/solarcity-maintains-34-residential-solar-market-share-in-1h-2015/406552/

And markets are very efficient at reward hacking. The point of any market is to multiply capital. If the only way to multiply the capital is through building solar, then you get solar. But the finance sector specializes in making the capital multiply as much as possible while doing as little as possible on the solar front. Huge chunks of those federal subsidies were gobbled up by junk-fees and other financial tricks – sometimes more than 100%.

The solar companies would be in even worse trouble, but they also tricked all their victims into signing binding arbitration waivers that deny them the power to sue and force them to have their grievances heard by fake judges who are paid by the solar companies to decide whether the solar companies have done anything wrong. You will not be surprised to learn that the arbitrators are reluctant to find against their paymasters.

I had a sense that all this was going on even before I read Semuels' excellent article. We bought a solar installation from Treeium, a highly rated, giant Southern California solar installer. We got an incredibly hard sell from them to get our solar "for free" – that is, through these financial arrangements – but I'd just sold a book and I had cash on hand and I was adamant that we were just going to pay upfront. As soon as that was clear, Treeium's ardor palpably cooled. We ended up with a grossly defective, unsafe and underpowered solar installation that has cost more than $10,000 to bring into a functional state (using another vendor). I briefly considered suing Treeium (I had insisted on striking the binding arbitration waiver from the contract) but in the end, I decided life was too short.

The thing is, solar is amazing. We love running our house on sunshine. But markets have proven – again and again – to be an unreliable and even dangerous way to improve Americans' homes and make them more resilient. After all, Americans' homes are the largest asset they are apt to own, which makes them irresistible targets for scammers:

https://pluralistic.net/2021/06/06/the-rents-too-damned-high/

That's why the subprime scammers targets Americans' homes in the 2000s, and it's why the house-stealing fraudsters who blanket the country in "We Buy Ugly Homes" are targeting them now. Same reason Willie Sutton robbed banks: "That's where the money is":

https://pluralistic.net/2023/05/11/ugly-houses-ugly-truth/

America can and should electrify and solarize. There are serious logistical challenges related to sourcing the underlying materials and deploying the labor, but those challenges are grossly overrated by people who assume the only way we can approach them is though markets, those monkey's paw curses that always find a way to snatch profitable defeat from the jaws of useful victory.

To get a sense of how the engineering challenges of electrification could be met, read McArthur fellow Saul Griffith's excellent popular engineering text Electrify:

https://pluralistic.net/2021/12/09/practical-visionary/#popular-engineering

And to really understand the transformative power of solar, don't miss Deb Chachra's How Infrastructure Works, where you'll learn that we could give every person on Earth the energy budget of a Canadian (like an American, but colder) by capturing just 0.4% of the solar rays that reach Earth's surface:

https://pluralistic.net/2023/10/17/care-work/#charismatic-megaprojects

But we won't get there with markets. All markets will do is create incentives to cheat. Think of the market for "carbon offsets," which were supposed to substitute markets for direct regulation, and which produced a fraud-riddled market for lemons that sells indulgences to our worst polluters, who go on destroying our planet and our future:

https://pluralistic.net/2021/04/14/for-sale-green-indulgences/#killer-analogy

We can address the climate emergency, but not by prompting the slow AI and hoping it doesn't figure out a way to reward-hack its way to giant profits while doing nothing. Founder and chairman of Goodleap, Hayes Barnard, is one of the 400 richest people in the world – a fortune built on scammers who tricked old people into signing away their homes for nonfunctional solar):

https://www.forbes.com/profile/hayes-barnard/?sh=40d596362b28

If governments are willing to spend billions incentivizing rooftop solar, they can simply spend billions installing rooftop solar – no Slow AI required.

Berliners: Otherland has added a second date (Jan 28 - TOMORROW!) for my book-talk after the first one sold out - book now!

If you'd like an essay-formatted version of this post to read or share, here's a link to it on pluralistic.net, my surveillance-free, ad-free, tracker-free blog:

https://pluralistic.net/2024/01/27/here-comes-the-sun-king/#sign-here

Back the Kickstarter for the audiobook of The Bezzle here!

Image:

Future Atlas/www.futureatlas.com/blog (modified)

https://www.flickr.com/photos/87913776@N00/3996366952

--

CC BY 2.0

https://creativecommons.org/licenses/by/2.0/

J Doll (modified)

https://commons.wikimedia.org/wiki/File:Blue_Sky_%28140451293%29.jpeg

CC BY 3.0

https://creativecommons.org/licenses/by/3.0/deed.en

[kingme1002]

Quantum computers:

leverage the principles of **quantum mechanics** (superposition, entanglement, and interference) to solve certain problems exponentially faster than classical computers. While still in early stages, they have transformative potential in multiple fields:

### **1. Cryptography & Cybersecurity**

- **Breaking Encryption**: Shor’s algorithm can factor large numbers quickly, threatening RSA and ECC encryption (forcing a shift to **post-quantum cryptography**).

- **Quantum-Safe Encryption**: Quantum Key Distribution (QKD) enables theoretically unhackable communication (e.g., BB84 protocol).

### **2. Drug Discovery & Material Science**

- **Molecular Simulation**: Modeling quantum interactions in molecules to accelerate drug design (e.g., protein folding, catalyst development).

- **New Materials**: Discovering superconductors, better batteries, or ultra-strong materials.

### **3. Optimization Problems**

- **Logistics & Supply Chains**: Solving complex routing (e.g., traveling salesman problem) for airlines, shipping, or traffic management.

- **Financial Modeling**: Portfolio optimization, risk analysis, and fraud detection.

### **4. Artificial Intelligence & Machine Learning**

- **Quantum Machine Learning (QML)**: Speeding up training for neural networks or solving complex pattern recognition tasks.

- **Faster Data Search**: Grover’s algorithm can search unsorted databases quadratically faster.

### **5. Quantum Chemistry**

- **Precision Chemistry**: Simulating chemical reactions at the quantum level for cleaner energy solutions (e.g., nitrogen fixation, carbon capture).

### **6. Climate & Weather Forecasting**

- **Climate Modeling**: Simulating atmospheric and oceanic systems with higher accuracy.

- **Energy Optimization**: Improving renewable energy grids or fusion reactor designs.

### **7. Quantum Simulations**

- **Fundamental Physics**: Testing theories in high-energy physics (e.g., quark-gluon plasma) or condensed matter systems.

### **8. Financial Services**

- **Option Pricing**: Monte Carlo simulations for derivatives pricing (quantum speedup).

- **Arbitrage Opportunities**: Detecting market inefficiencies faster.

### **9. Aerospace & Engineering**

- **Aerodynamic Design**: Optimizing aircraft shapes or rocket propulsion systems.

- **Quantum Sensors**: Ultra-precise navigation (e.g., GPS-free positioning).

### **10. Breakthroughs in Mathematics**

- **Solving Unsolved Problems**: Faster algorithms for algebraic geometry, topology, or number theory.

[june-gdprototyping]

CNC development history and processing principles

CNC machine tools are also called Computerized Numerical Control (CNC for short). They are mechatronics products that use digital information to control machine tools. They record the relative position between the tool and the workpiece, the start and stop of the machine tool, the spindle speed change, the workpiece loosening and clamping, the tool selection, the start and stop of the cooling pump and other operations and sequence actions on the control medium with digital codes, and then send the digital information to the CNC device or computer, which will decode and calculate, issue instructions to control the machine tool servo system or other actuators, so that the machine tool can process the required workpiece.

‌1. The evolution of CNC technology: from mechanical gears to digital codes

The Beginning of Mechanical Control (late 19th century - 1940s)

The prototype of CNC technology can be traced back to the invention of mechanical automatic machine tools in the 19th century. In 1887, the cam-controlled lathe invented by American engineer Herman realized "programmed" processing for the first time by rotating cams to drive tool movement. Although this mechanical programming method is inefficient, it provides a key idea for subsequent CNC technology. During World War II, the surge in demand for military equipment accelerated the innovation of processing technology, but the processing capacity of traditional machine tools for complex parts had reached a bottleneck.

The electronic revolution (1950s-1970s)

After World War II, manufacturing industries mostly relied on manual operations. After workers understood the drawings, they manually operated machine tools to process parts. This way of producing products was costly, inefficient, and the quality was not guaranteed. In 1952, John Parsons' team at the Massachusetts Institute of Technology (MIT) developed the world's first CNC milling machine, which input instructions through punched paper tape, marking the official birth of CNC technology. The core breakthrough of this stage was "digital signals replacing mechanical transmission" - servo motors replaced gears and connecting rods, and code instructions replaced manual adjustments. In the 1960s, the popularity of integrated circuits reduced the size and cost of CNC systems. Japanese companies such as Fanuc launched commercial CNC equipment, and the automotive and aviation industries took the lead in introducing CNC production lines. 

Integration of computer technology (1980s-2000s)

With the maturity of microprocessor and graphical interface technology, CNC entered the PC control era. In 1982, Siemens of Germany launched the first microprocessor-based CNC system Sinumerik 800, whose programming efficiency was 100 times higher than that of paper tape. The integration of CAD (computer-aided design) and CAM (computer-aided manufacturing) software allows engineers to directly convert 3D models into machining codes, and the machining accuracy of complex surfaces reaches the micron level. During this period, equipment such as five-axis linkage machining centers came into being, promoting the rapid development of mold manufacturing and medical device industries.

Intelligence and networking (21st century to present)

The Internet of Things and artificial intelligence technologies have given CNC machine tools new vitality. Modern CNC systems use sensors to monitor parameters such as cutting force and temperature in real time, and use machine learning to optimize processing paths. For example, the iSMART Factory solution of Japan's Mazak Company achieves intelligent scheduling of hundreds of machine tools through cloud collaboration. In 2023, the global CNC machine tool market size has exceeded US$80 billion, and China has become the largest manufacturing country with a production share of 31%.

2. CNC machining principles: How code drives steel

The essence of CNC technology is to convert the physical machining process into a control closed loop of digital signals. Its operation logic can be divided into three stages:

Geometric Modeling and Programming

After building a 3D model using CAD software such as UG and SolidWorks, CAM software “deconstructs” the model: automatically calculating parameters such as tool path, feed rate, spindle speed, and generating G code (such as G01 X100 Y200 F500 for linear interpolation to coordinates (100,200) and feed rate 500mm/min). Modern software can even simulate the material removal process and predict machining errors.

Numerical control system analysis and implementation

The "brain" of CNC machine tools - the numerical control system (such as Fanuc 30i, Siemens 840D) converts G codes into electrical pulse signals. Taking a three-axis milling machine as an example, the servo motors of the X/Y/Z axes receive pulse commands and convert rotary motion into linear displacement through ball screws, with a positioning accuracy of up to ±0.002mm. The closed-loop control system uses a grating ruler to feedback position errors in real time, forming a dynamic correction mechanism.

Multi-physics collaborative control

During the machining process, the machine tool needs to coordinate multiple parameters synchronously: the spindle motor drives the tool to rotate at a high speed of 20,000 rpm, the cooling system sprays atomized cutting fluid to reduce the temperature, and the tool changing robot completes the tool change within 0.5 seconds. For example, when machining titanium alloy blades, the system needs to dynamically adjust the cutting depth according to the hardness of the material to avoid tool chipping.

‌3. The future of CNC technology: cross-dimensional breakthroughs and industrial transformation

Currently, CNC technology is facing three major trends:

‌Combined‌: Turning and milling machine tools can complete turning, milling, grinding and other processes on one device, reducing clamping time by 90%;

Additive-subtractive integration: Germany's DMG MORI's LASERTEC series machine tools combine 3D printing and CNC finishing to directly manufacture aerospace engine combustion chambers;

‌Digital Twin‌: By using a virtual machine tool to simulate the actual machining process, China's Shenyang Machine Tool's i5 system has increased debugging efficiency by 70%.

From the meshing of mechanical gears to the flow of digital signals, CNC technology has rewritten the underlying logic of the manufacturing industry in 70 years. It is not only an upgrade of machine tools, but also a leap in the ability of humans to transform abstract thinking into physical entities. In the new track of intelligent manufacturing, CNC technology will continue to break through the limits of materials, precision and efficiency, and write a new chapter for industrial civilization.

[nainad123]

Driving Training Simulator Market Key Segmentation, Growth, Top Key Players and Forecast by 2034

The global driving training simulators market is poised to reach a valuation of $2,120.2 million in 2024, with expectations to grow at a CAGR of 4.7% throughout the forecast period, reaching $3,356.2 million by the end of 2034. This growth is driven by increasing demand for immersive and technologically advanced learning experiences in driving education.

Advancements in virtual reality (VR) and augmented reality (AR) technologies, coupled with a focus on cost-effective and safe training methods, are pivotal factors driving the market. These technologies enable realistic and effective simulations for trainees, enhancing learning outcomes.

However, the market faces challenges such as high initial setup costs, the necessity for advanced equipment, and ongoing upgrades to maintain simulation relevance. Additionally, aligning training materials and meeting diverse requirements present further hurdles. Addressing these challenges and ensuring market sustainability will require industry collaboration, continuous innovation, and adaptable solutions.

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Key Takeaways from the Market Study:

In 2019, the global driving training simulator market was valued at US$ 1,728.8 million.

By 2024, North America is expected to hold a market share of 45.8% in the driving training simulator market.

East Asia is projected to account for 32.7% of the global market by 2024.

The U.S. market is anticipated to reach a valuation of US$ 731.2 million in 2024, with a market share of 75.3%.

In China, the driving training simulator market is expected to be worth US$ 499.9 million in 2024.

The compact simulator segment is estimated to achieve a market share of 43.2% by 2024.

 “Increasing focus on immersive, tech-driven learning experiences is boosting the market for driving training simulators. The market is driven ahead by rising demand for safe and affordable driver training, which guarantee realistic and effective simulations for trainees,” remarks a Fact.MR analyst.

Competitive Landscape:

Major companies and startups in the global driving training simulator market are strategically leveraging cutting-edge technologies to achieve scalability. The integration of VR and AI has significantly enhanced the development of immersive simulations, improving training effectiveness. Companies are employing advanced sensor emulation and machine learning techniques to focus on training autonomous vehicles.

To enhance scalability, businesses are integrating cloud-based technologies and leveraging 5G connectivity across their operations. Driving training simulators have evolved due to advancements in software, hardware, and networking, meeting the increasing demand for efficient and rapid training without compromising safety or efficacy.

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Key developments in the market include:

In 2023, Ansible Motion and its parent company, AB Dynamics, a global provider of automotive test equipment, completed the merger of their Driver-in-the-Loop (DIL) car simulation businesses. Plans are underway to expand Ansible Motion's R&D and production headquarters in Norfolk, UK, while maintaining a satellite plant at AB Dynamics' current location in Bradford-on-Avon, UK. The integrated simulator company will operate under the Ansible Motion name, supported by a growing network of regional offices providing local sales and technical support in North America, Europe, and Asia-Pacific.

Also in 2023, PREMA Racing selected Cruden's Panthera Simulation Software Suite with Unity rendering for its new simulator. This decision enables engineers and racing drivers to utilize advanced simulators for race car development and setup across various race programs, including Formula 2 and the new Lamborghini LMDh campaign. Simultaneously, PREMA is upgrading its original simulator with the Panthera platform to ensure consistency and efficiency in their training and development processes.

Segmentation of Driving Training Simulator Market Research

By Simulator Type:

Compact

Full-scale

Advanced

By End Use:

Automotive

Marine

Aviation

By Region:

North America

Eastern Europe

Western Europe

Latin America

East Asia

South Asia & Oceania

Middle East & Africa

𝐂𝐨𝐧𝐭𝐚𝐜𝐭:

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[mastergarryblogs]

Video as a Sensor Market Report: Opportunities, Challenges & Projections

Accelerating Intelligence: The Rise of Video as a Sensor Technology

We are witnessing a transformation in how machines perceive the world. The global Video as a Sensor market is advancing rapidly, driven by breakthroughs in edge computing, machine learning, and real-time video analytics. video as a sensor market is no longer confined to traditional video surveillance; it now serves as a dynamic, sensor-based system for intelligent decision-making across diverse industries. From optimizing urban traffic to enabling autonomous navigation, VaaS is a foundational layer of next-generation intelligent infrastructure.

By 2031, the Video as a Sensor market is projected to soar to USD 101.91 billion, growing at a robust CAGR of 8%, up from USD 69.72 billion in 2023. This trajectory is fueled by the demand for automation, real-time analytics, and safer environments.

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Intelligent Video Analytics: Enabling Real-Time Situational Awareness

VaaS leverages video streams as rich data sources. Through embedded AI algorithms, these systems detect and analyze objects, behaviors, and environments—eliminating the need for additional sensor hardware. This shift towards intelligent visual perception is enhancing operations in mission-critical industries such as:

Public Safety: Automated threat recognition and proactive alert systems.

Retail: Heat mapping, customer journey tracking, and loss prevention.

Healthcare: Patient monitoring, anomaly detection, and contactless diagnostics.

Transportation: Traffic flow optimization, vehicle classification, and pedestrian safety.

Manufacturing: Equipment monitoring, quality inspection, and workplace safety.

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Video as a Sensor Market Segmentation and Strategic Insights

By Type: Standalone vs. Integrated Video Sensors

Standalone Video Sensors offer edge-based intelligence, allowing immediate processing without dependence on centralized systems. Their advantages include:

Reduced latency

Lower bandwidth usage

Enhanced privacy

Ideal for retail stores, small-scale surveillance, and localized analytics

Integrated Video Sensors incorporate multi-modal data inputs. They combine visual data with thermal, motion, acoustic, and even LiDAR sensors to provide a more comprehensive picture. Applications include:

Autonomous Vehicles: Real-time fusion of vision and radar data

Industrial Automation: Hazard detection and predictive maintenance

Smart Cities: Integrated environment and crowd monitoring

By Material: Components Driving Performance and Durability

High-performance materials play a pivotal role in ensuring the reliability and longevity of video sensors. The key components include:

Camera Lenses: Engineered from precision optical glass or polycarbonate for clarity and high zoom capabilities.

Semiconductor Materials: CMOS sensors dominate due to their power efficiency and speed, supporting high-frame-rate video and integration with AI accelerators.

Plastic & Metal Casings: Rugged enclosures designed for outdoor and industrial environments, supporting IP67/IP68 ratings and thermal regulation.

These innovations not only enhance video quality but also reduce device footprint and operational costs.

By End-User: Sector-Wide Transformation Through Video as a Sensor Market

1. Security and Surveillance

Automatic license plate recognition (ALPR)

Biometric identification (face, gait, posture)

Perimeter breach detection

Crowd density analysis

2. Retail

Queue management systems

Shopper intent prediction

Stock-out alerts and planogram compliance

Behavioral segmentation

3. Automotive

Adaptive cruise control and lane-keeping

360-degree situational awareness

Driver monitoring systems (DMS)

Smart parking automation

4. Healthcare

Non-intrusive patient surveillance

Elderly fall detection

Remote surgery and diagnostic imaging

Infection control via contact tracing

5. Smart Cities

Traffic light optimization

Illegal dumping and graffiti detection

Air quality monitoring via visual indicators

Emergency response coordination

Regional Video as a Sensor Market Outlook: A Global Wave of Adoption

North America

With a mature tech ecosystem and strong surveillance infrastructure, North America remains a leader in adopting advanced VaaS systems, especially for homeland security, smart policing, and industrial automation.

Asia-Pacific

Rapid urbanization and significant investments in smart city projects across China, Japan, and India position this region as the fastest-growing VaaS market. Automotive and manufacturing sectors serve as major adoption verticals.

Europe

Driven by stringent GDPR compliance and sustainability goals, Europe emphasizes privacy-focused AI video analytics. Intelligent transportation systems (ITS) and energy-efficient smart buildings are driving demand.

Middle East and Africa

Massive infrastructure initiatives and security upgrades are propelling demand. VaaS is gaining traction in oil facilities, public safety, and tourism hubs.

South America

Emerging VaaS applications in agriculture (precision farming), logistics, and crime detection are gaining momentum as governments and enterprises modernize legacy systems.

Competitive Landscape: Leaders in Video Intelligence

The competitive environment is shaped by innovation in AI chips, edge processors, and scalable cloud architectures. Major players include:

Hikvision – AI-powered surveillance and edge computing

Bosch Security Systems – Integrated security platforms

Axis Communications – Smart network cameras with in-built analytics

Honeywell International – Industrial-grade video intelligence

FLIR Systems – Thermal and multi-sensor fusion

Sony & Samsung Electronics – High-resolution CMOS sensors

Qualcomm, Intel, NVIDIA – AI chipsets and embedded vision

Google Cloud & AWS – VaaS via scalable, cloud-native platforms

Smaller innovators and startups are also disrupting the field with niche capabilities in facial recognition, retail analytics, and edge-AI chipsets.

Future Outlook: Pathways to Intelligent Automation

The future of the Video as a Sensor market is shaped by convergence and miniaturization. We anticipate:

Edge-AI Proliferation: Microprocessors integrated directly into cameras

5G-Enabled Real-Time Processing: Enabling ultra-low latency video transmission

Privacy-Preserving AI: Federated learning and on-device encryption

Autonomous Monitoring: Drones and mobile robots using vision as their primary sense

These trends position video as not just a sensor but as a strategic tool for perception, prediction, and control in an increasingly automated world.

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Conclusion

The evolution of Video as a Sensor technology is redefining how industries sense, interpret, and act. As AI-driven video analysis becomes a core enabler of digital transformation, the Video as a Sensor market is poised to be one of the most impactful sectors of the coming decade. Enterprises and governments that invest early in scalable, intelligent video infrastructure will gain unprecedented advantages in efficiency, security, and operational agility.

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[amrutmnm]

Airborne Optronics Market Share Analysis: Leading Players & Regional Insights

The Airborne Optronics Market is experiencing rapid expansion, fueled by increasing demand for advanced electro-optical systems in military, commercial, and urban air mobility applications. The market, valued at USD 1.4 billion in 2020, is projected to reach USD 2.5 billion by 2025, growing at a CAGR of 12.7%. As defense forces, security agencies, and commercial aviation sectors adopt cutting-edge imaging and surveillance technologies, airborne optronics is becoming an integral part of modern aircraft and UAV systems.

Technological advancements, rising investments in defense modernization, and the growing use of unmanned aerial vehicles (UAVs) for surveillance and reconnaissance are among the key factors driving the market forward. Additionally, the expansion of urban air mobility (UAM) platforms, including air taxis and autonomous cargo drones, is expected to increase the demand for advanced airborne imaging and detection systems.

The Impact of COVID-19 on the Airborne Optronics Market

The COVID-19 pandemic had a notable impact on the Airborne Optronics Market, leading to disruptions in supply chains and temporary slowdowns in aircraft production. In 2020, aircraft orders and deliveries declined, affecting demand for airborne optronics. Industry experts estimated that the pandemic resulted in a 7–10% impact on production and services worldwide.

Despite these challenges, recovery began in early 2021 as aerospace manufacturers resumed operations. The increased reliance on UAVs for autonomous surveillance, border security, and ISR (intelligence, surveillance, and reconnaissance) missions contributed to the resurgence of demand for airborne optronics. Post-pandemic, investments in next-generation imaging sensors and AI-driven surveillance systems are expected to accelerate market growth.

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Technological Innovations Reshaping the Airborne Optronics Market

Modern airborne optronics systems are evolving with advancements in multispectral imaging, AI-based analytics, and high-resolution electro-optical sensors. These innovations are enhancing the capabilities of aircraft, UAVs, and airborne platforms for defense and commercial applications. The development of miniaturized electro-optical systems through nanotechnology is allowing for integration into small drones and lightweight aerial vehicles, improving efficiency without compromising performance.

Multispectral and hyperspectral imaging technologies are also advancing, allowing airborne systems to detect and analyze objects with greater precision across multiple wavelengths. This enhanced imaging capability is crucial for applications in border security, environmental monitoring, and military reconnaissance. As a result, the multispectral segment is expected to experience the highest growth during the forecast period due to its affordability and adaptability in UAV-based applications.

The integration of artificial intelligence (AI) and machine learning (ML) in airborne optronics is transforming the way data is processed and analyzed. AI-powered imaging systems can automatically detect, classify, and track objects, providing real-time intelligence for military and commercial operators. These advancements are making airborne surveillance more efficient and reducing human intervention in complex decision-making scenarios.

Opportunities Driving the Airborne Optronics Market

The rapid development of urban air mobility (UAM) platforms presents a significant growth opportunity for the Airborne Optronics Market. The demand for autonomous aerial transportation, including air taxis, air ambulances, and cargo drones, is rising. The urban air mobility market is projected to expand from USD 2.5 billion to USD 4.9 billion by 2025, creating a strong demand for highly advanced airborne sensor technologies. Ensuring safe navigation, obstacle detection, and precise landing in urban environments will require state-of-the-art electro-optical systems, further boosting market demand.

Another major opportunity lies in the increasing deployment of UAVs in the military sector. UAVs are being integrated with high-resolution electro-optics and infrared (EO/IR) sensors to conduct surveillance operations, border security, and battlefield intelligence missions. The growing need for autonomous surveillance solutions and drone-based reconnaissance is expected to drive continued investments in airborne optronics technologies.

As the market for commercial drones expands, various industries are exploring applications beyond surveillance, such as infrastructure inspections, environmental monitoring, and emergency response. Drones equipped with airborne optronics are increasingly being used in commercial settings to provide real-time imaging, terrain mapping, and asset monitoring, further widening the scope of market opportunities.

Challenges in the Airborne Optronics Market

Despite its growth potential, the Airborne Optronics Market faces several challenges, particularly regarding weather-related inefficiencies in electro-optical sensors. Airborne electro-optics systems are highly sensitive to environmental conditions such as fog, haze, smoke, and dust, which can obscure visibility and limit the accuracy of imaging and targeting systems.

In battlefield scenarios, airborne optronics systems must operate in unpredictable environments where visibility conditions change rapidly. Factors such as smoke from explosions, dust clouds, and atmospheric interference can reduce the effectiveness of EO/IR sensors, making it challenging to maintain consistent surveillance. Addressing these limitations through enhanced sensor technology, adaptive imaging algorithms, and multi-sensor integration is essential for improving system performance in extreme conditions.

Another challenge is the increasing threat of cybersecurity risks associated with airborne optronics systems. As military and commercial aircraft become more dependent on digital imaging and real-time data processing, the potential for cyber attacks, hacking, and electronic warfare interference is rising. Ensuring the security of real-time surveillance data and sensor networks is a critical concern for both defense agencies and commercial operators.

Regional Market Insights: North America Leading Growth

North America is currently the largest regional market for airborne optronics, driven by strong defense spending, technological leadership, and a growing fleet of commercial and military aircraft. The United States remains a dominant player, with major defense contractors and aerospace companies actively investing in next-generation electro-optical systems. Companies like Northrop Grumman, Lockheed Martin, L3Harris Technologies, and Collins Aerospace are at the forefront of innovation in the airborne optronics sector.

In Europe, nations such as France, Germany, and the UK are focusing on modernizing their defense and surveillance capabilities. The presence of leading European companies, including Thales SA and Safran, is contributing to the region’s market expansion. Meanwhile, the Asia-Pacific region is experiencing growing demand for airborne optronics, particularly in countries like China, India, and Japan, where investments in UAV technology and military surveillance systems are rising.

The Middle East, Africa, and Latin America are emerging markets for airborne optronics, with increasing investments in border security, counter-terrorism, and urban surveillance applications. These regions are expected to witness steady growth as governments and private sector players explore new airborne imaging and reconnaissance solutions.

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Future Outlook for the Airborne Optronics Market

The Airborne Optronics Market is expected to witness strong growth, driven by advancements in imaging technology, increasing defense budgets, and the rising demand for UAV-based surveillance. The integration of AI, multispectral imaging, and adaptive sensor technologies will continue to shape the future of airborne surveillance and reconnaissance.

As defense agencies, security forces, and commercial operators seek highly efficient and reliable airborne imaging solutions, companies investing in next-generation electro-optical systems will remain at the forefront of market growth. The continued expansion of urban air mobility and autonomous aircraft systems is expected to open new opportunities for airborne optronics technology, ensuring its relevance in the evolving aerospace industry.

[govindhtech]

Agilex 3 FPGAs: Next-Gen Edge-To-Cloud Technology At Altera

Agilex 3 FPGA

Today, Altera, an Intel company, launched a line of FPGA hardware, software, and development tools to expand the market and use cases for its programmable solutions. Altera unveiled new development kits and software support for its Agilex 5 FPGAs at its annual developer’s conference, along with fresh information on its next-generation, cost-and power-optimized Agilex 3 FPGA.

Altera

Why It Matters

Altera is the sole independent provider of FPGAs, offering complete stack solutions designed for next-generation communications infrastructure, intelligent edge applications, and high-performance accelerated computing systems. Customers can get adaptable hardware from the company that quickly adjusts to shifting market demands brought about by the era of intelligent computing thanks to its extensive FPGA range. With Agilex FPGAs loaded with AI Tensor Blocks and the Altera FPGA AI Suite, which speeds up FPGA development for AI inference using well-liked frameworks like TensorFlow, PyTorch, and OpenVINO toolkit and tested FPGA development flows, Altera is leading the industry in the use of FPGAs in AI inference workload

Intel Agilex 3

What Agilex 3 FPGAs Offer

Designed to satisfy the power, performance, and size needs of embedded and intelligent edge applications, Altera today revealed additional product details for its Agilex 3 FPGA. Agilex 3 FPGAs, with densities ranging from 25K-135K logic elements, offer faster performance, improved security, and higher degrees of integration in a smaller box than its predecessors.

An on-chip twin Cortex A55 ARM hard processor subsystem with a programmable fabric enhanced with artificial intelligence capabilities is a feature of the FPGA family. Real-time computation for time-sensitive applications such as industrial Internet of Things (IoT) and driverless cars is made possible by the FPGA for intelligent edge applications. Agilex 3 FPGAs give sensors, drivers, actuators, and machine learning algorithms a smooth integration for smart factory automation technologies including robotics and machine vision.

Agilex 3 FPGAs provide numerous major security advancements over the previous generation, such as bitstream encryption, authentication, and physical anti-tamper detection, to fulfill the needs of both defense and commercial projects. Critical applications in industrial automation and other fields benefit from these capabilities, which guarantee dependable and secure performance.

Agilex 3 FPGAs offer a 1.9×1 boost in performance over the previous generation by utilizing Altera’s HyperFlex architecture. By extending the HyperFlex design to Agilex 3 FPGAs, high clock frequencies can be achieved in an FPGA that is optimized for both cost and power. Added support for LPDDR4X Memory and integrated high-speed transceivers capable of up to 12.5 Gbps allow for increased system performance.

Agilex 3 FPGA software support is scheduled to begin in Q1 2025, with development kits and production shipments following in the middle of the year.

How FPGA Software Tools Speed Market Entry

Quartus Prime Pro

The Latest Features of Altera’s Quartus Prime Pro software, which gives developers industry-leading compilation times, enhanced designer productivity, and expedited time-to-market, are another way that FPGA software tools accelerate time-to-market. With the impending Quartus Prime Pro 24.3 release, enhanced support for embedded applications and access to additional Agilex devices are made possible.

Agilex 5 FPGA D-series, which targets an even wider range of use cases than Agilex 5 FPGA E-series, which are optimized to enable efficient computing in edge applications, can be designed by customers using this forthcoming release. In order to help lower entry barriers for its mid-range FPGA family, Altera provides software support for its Agilex 5 FPGA E-series through a free license in the Quartus Prime Software.

Support for embedded applications that use Altera’s RISC-V solution, the Nios V soft-core processor that may be instantiated in the FPGA fabric, or an integrated hard-processor subsystem is also included in this software release. Agilex 5 FPGA design examples that highlight Nios V features like lockstep, complete ECC, and branch prediction are now available to customers. The most recent versions of Linux, VxWorks, and Zephyr provide new OS and RTOS support for the Agilex 5 SoC FPGA-based hard processor subsystem.

How to Begin for Developers

In addition to the extensive range of Agilex 5 and Agilex 7 FPGAs-based solutions available to assist developers in getting started, Altera and its ecosystem partners announced the release of 11 additional Agilex 5 FPGA-based development kits and system-on-modules (SoMs).

Developers may quickly transition to full-volume production, gain firsthand knowledge of the features and advantages Agilex FPGAs can offer, and easily and affordably access Altera hardware with FPGA development kits.

Kits are available for a wide range of application cases and all geographical locations. To find out how to buy, go to Altera’s Partner Showcase website.

Read more on govindhtech.com

[omkarpatel]

The Rise of 3D Printing in Prosthetics and Orthotics Market

The global prosthetics and orthotics market plays a vital role in improving quality of life for millions worldwide. Worth an estimated $7.2 billion in 2024, the market facilitates mobility for those with limb differences or injuries through highly customized external limb replacements and braces. The market introduces prosthetics and orthotics—Medical devices that enhance or assist impaired body parts and mobility. Orthotics are braces or supports for joints, spine, and limbs; prosthetics externally replace missing limbs. Together they improve functionality and quality of life for users. Major players in the prosthetics and orthotics space utilizing advanced manufacturing include Ossur, Steeper Group, Blatchford, Fillauer, Ottobock, and WillowWood Global. These industry leaders increasingly deploy cutting-edge 3D printing and customized design software to produce state-of-the-art prosthetics and braces. Current trends in the prosthetics and orthotics market include growing utilization of 3D printing and advanced manufacturing techniques. 3D printing enables on-demand production of complex, customized devices. It reduces manufacturing costs and wait times while improving fit and comfort. Expanding material options also allow more lifelike prosthetics. As technology evolves, the market is positioned for continued growth through 2031 in facilitating mobility worldwide. Future Outlook The prosthetics and orthotics market is expected to witness significant advancements in the coming years. Manufacturers are constantly focusing on developing innovative technologies such as 3D printed prosthetics that provide a better fit, enhanced comfort, and unrestricted movement. There is also a rising trend of using lightweight, highly durable and comfortable materials like carbon fiber and thermoplastics to manufacture prosthetic devices. Advancements in myoelectric prosthetics with touch and motion sensors are making them more dexterous and responsive. Using pattern recognition and machine learning techniques, next-gen prosthetics could gain functionality approaching that of natural limbs.

PEST Analysis Political: Regulations regarding clinical trials and approvals of new prosthetic technologies may affect market growth. Favorable reimbursement policies for prosthetic devices can boost adoption. Economic: Rising disposable incomes allow more individuals to opt for higher-end prosthetics. Emerging markets present abundant opportunities for growth. Inflation and economic slowdowns can hinder market profitability. Social: Increasing incidence of amputations and disabilities due to aging population, accidents, war injuries etc. drive market demand. Growing awareness regarding prosthetics and orthotics aids adoption. Stigma associated with limb loss poses challenges. Technological: Advancements in materials, manufacturing techniques like 3D printing, sensors, computing power and battery technologies are enhancing functionality and usability of prosthetics/orthotics. Myoelectric and robotic prosthetics have vastly improved in recent years. Opportunity Rising aging population presents a huge opportunity for prosthetics and orthotics targeting mobility issues and disabilities. Over 630,000 amputations occur annually in the U.S. due to dysvascular conditions like diabetes, presenting a sizable patient pool. Expanding applications of prosthetics and orthotics beyond mobility impairment into sports and military could drive significant growth. Growing incidence of trauma and injuries globally increases the number of patients relying on these devices. Emerging markets like Asia Pacific and Latin America offer immense opportunities owing to increasing disposable incomes, expanding healthcare infrastructure and rising medical tourism. Technological advancements are constantly improving functionality and usability of prosthetic devices, fueling adoption rates. The lightweight, durable and comfortable characteristics of newer materials expand addressable indications and patient acceptance. Key Takeaways Growing demand from aging population: The rapid increase in aging population worldwide who are prone to mobility issues, disabilities and chronic diseases like diabetes is a key driver spurring sales of orthotic and prosthetic devices. Global expansion into emerging markets: Emerging markets like Asia Pacific, Latin America, Eastern Europe and the Middle East offer immense opportunities owing to their large population bases and improving healthcare penetration. Technological advancements: Constant R&D bringing advancements in areas such as 3D printing, lightweight materials,

[smbmatters]

Exploring the Latest Breakthroughs in Technology

Introduction

Technology is evolving at a rapid pace, bringing with it groundbreaking innovations that are reshaping our world. From artificial intelligence to renewable energy solutions, these advancements are enhancing our lives in ways we never imagined. In this article, we'll explore some of the most exciting recent breakthroughs in technology that are set to transform various industries and everyday life.

1. Artificial Intelligence and Machine Learning

Artificial Intelligence (AI) and Machine Learning (ML) are at the forefront of technological innovation. AI and ML are being integrated into a myriad of applications, from healthcare diagnostics to personalized marketing. These technologies analyze vast amounts of data to make predictions, automate processes, and provide valuable insights.

AI in Healthcare

AI is revolutionizing healthcare by improving diagnostic accuracy and patient care. Machine learning algorithms can analyze medical images to detect diseases like cancer at early stages, enabling timely treatment and better patient outcomes.

AI in Everyday Life

In our daily lives, AI powers virtual assistants like Siri and Alexa, enhances customer service through chat-bots, and personalizes our online shopping experiences. The continuous improvement of AI algorithms is making these applications smarter and more efficient.

2. Quantum Computing

Quantum Computing promises to solve problems that are currently insurmountable for classical computers. By leveraging the principles of quantum mechanics, quantum computers perform complex calculations at unprecedented speeds.

Advancements in Cryptography

Quantum computing has the potential to revolutionize cryptography by breaking encryption codes that secure our digital communications. This breakthrough necessitates the development of new cryptographic methods to protect sensitive information.

Applications in Drug Discovery

In the pharmaceutical industry, quantum computing can simulate molecular interactions at a granular level, accelerating the drug discovery process and leading to the development of new, effective medications.

3. Renewable Energy Technologies

The shift towards renewable energy technologies is crucial in combating climate change. Innovations in solar, wind, and battery technologies are making renewable energy more efficient and accessible.

Solar and Wind Energy

Recent advancements in solar panel efficiency and wind turbine design are increasing the amount of energy harvested from natural sources. These improvements are making renewable energy a viable alternative to fossil fuels.

Energy Storage Solutions

Enhanced battery technologies are crucial for storing renewable energy, ensuring a consistent power supply even when the sun isn't shining or the wind isn't blowing. Breakthroughs in battery capacity and lifespan are driving the adoption of renewable energy systems.

4. Internet of Things (IoT)

The Internet of Things (IoT) connects devices and systems, enabling them to communicate and share data. This connectivity is transforming homes, industries, and cities into smarter, more efficient environments.

Smart Homes

IoT technology is making homes smarter by automating lighting, heating, and security systems. Smart home devices can be controlled remotely, offering convenience and energy savings.

Industrial IoT

In industrial settings, IoT devices monitor equipment health and optimize manufacturing processes. Predictive maintenance enabled by IoT sensors can reduce downtime and improve efficiency.

5. Blockchain Technology

Blockchain is revolutionizing how we handle transactions and data security. This decentralized ledger technology ensures transparency and security in various applications.

Financial Transactions

Blockchain is streamlining financial transactions by eliminating the need for intermediaries. It provides a secure and transparent way to transfer funds and verify transactions.

Supply Chain Management

In supply chains, blockchain offers traceability and transparency, reducing fraud and ensuring the authenticity of products. This technology is particularly beneficial in industries like pharmaceuticals and food.

6. 5G Technology

The roll-out of 5G technology is set to enhance connectivity with faster speeds and lower latency. This advancement will support the growth of IoT, autonomous vehicles, and smart cities.

Enhanced Mobile Connectivity

5G technology promises to improve mobile experiences with seamless streaming and quick downloads. It will also enable new applications in virtual and augmented reality.

Smart Cities

5G will facilitate the development of smart cities, where real-time data exchange enhances urban management systems, traffic control, and emergency services.

7. Autonomous Vehicles

Autonomous vehicles are set to transform transportation. Advances in AI and sensor technology are bringing self-driving cars closer to reality, offering safer and more efficient travel options.

Safety and Efficiency

Autonomous vehicles can reduce accidents caused by human error and optimize traffic flow, reducing congestion and emissions. They hold the potential to revolutionize the logistics and delivery sectors.

Delivery Services

Self-driving delivery vehicles and drones are making logistics faster and more reliable. These innovations are particularly beneficial in urban areas, where they can reduce traffic and pollution.

8. Biotechnology

Biotechnology is advancing rapidly, offering solutions in healthcare, agriculture, and environmental management. Innovations in gene editing, synthetic biology, and bio-engineering are opening new possibilities.

Gene Editing

CRISPR technology is enabling precise gene editing, offering potential cures for genetic diseases and innovations in agriculture. This technology is paving the way for new treatments and sustainable farming practices.

Synthetic Biology

Synthetic biology is creating new biological systems and organisms, leading to advancements in medicine, bio-fuels, and sustainable materials. This field holds promise for addressing global challenges such as disease and climate change.

9. Augmented Reality (AR) and Virtual Reality (VR)

AR and VR technologies are providing immersive experiences in entertainment, education, and various professional fields. These technologies are creating new ways to interact with digital content.

Gaming and Entertainment

AR and VR are enhancing gaming experiences by creating immersive environments and interactive game-play. These technologies are also being used in movies and virtual concerts, offering new forms of entertainment.

Professional Training

In education and professional training, AR and VR offer realistic simulations for hands-on learning. Fields like medicine, engineering, and aviation benefit from these technologies by providing safe and effective training environments.

Conclusion

The latest breakthroughs in technology are driving significant changes across various sectors. From AI and quantum computing to renewable energy and autonomous vehicles, these innovations are shaping the future and improving our lives. Staying informed about these developments is crucial for individuals and businesses alike to leverage the benefits of these technological advancements. As we look to the future, these game-changing technologies will continue to evolve, offering new opportunities and solutions to the challenges we face.

[dishachrista]

Discovering the Wonders of Machine Learning and Its Advantages:

Have you ever wondered how computers can learn and make choices all by themselves? It's like teaching them to be smart on their own. That's what machine learning is, and it's changing the way we do things. Let's take a journey to find out why machine learning is so amazing. We'll talk about things like helping computers understand languages, predicting the weather, and even teaching them to drive cars! So, let's explore the magic of machine learning.

The Good Things About Machine Learning:

Understanding InformationThink of machine learning as a super-smart helper that can deal with really big and complicated sets of information. Humans might get confused with all that data, but machine learning can assist us in making decisions based on it.

Changing the GameImagine you have a giant puzzle with lots of pieces. You know they make a picture, but it's just too hard to put it together by yourself. Machine learning is like a super-fast puzzle solver. It not only puts the puzzle together but also shows you amazing things about the picture that you might have missed.

Making Tasks SimpleOne cool thing about machine learning is that it can do boring and repetitive tasks for us. Think about typing lots of numbers into a computer spreadsheet – it can do that for us, and it hardly ever makes mistakes. This means we can use our time for more fun stuff.

Getting Things RightMachine learning models are really good at certain jobs. For example, they can tell if an email is spam much better than we can. That's super useful because we don't want our email inboxes to be filled with spam. It's like having a superhero to keep our emails clean.

Learning and Getting BetterThe more a machine learning model sees, the smarter it becomes. It learns from new information and gets better at what it does. This is really helpful in jobs where things change a lot, like predicting the stock market or understanding what people are talking about on social media.

Personalizing EverythingHave you ever noticed that websites like Netflix or Amazon suggest things you might like? That's machine learning in action. It watches what you do and recommends things you'll enjoy. It's like having a personal shopper who knows your tastes.

Handling Lots of StuffMachine learning models are like super chefs who can cook for a huge party. They can manage a massive amount of data and make quick decisions. This is really important for things like banks, which need to handle lots of transactions quickly and accurately.

Saving Time and MoneyMachine learning can save businesses a ton of time and money. For example, it can predict when machines in a factory might break, so they can be fixed before they cause big problems. This saves a lot of money because it's cheaper to fix things before they break.

Solving Tricky ProblemsSome problems are really tough, like understanding different languages, recognizing objects in pictures, or even playing complicated games. Machine learning can take on these challenges and find solutions that might be hard for humans.

Learning from AnythingMachine learning can learn from almost anything, even from things that don't seem like regular data. For example, it can learn from written words, pictures, or videos. This helps businesses understand what people think and like.

Quick Decision-MakingImagine being in a self-driving car. It needs to make fast decisions to keep you safe. Machine learning helps it do that by processing data from sensors and making decisions in real-time.

Creating Cool ThingsMachine learning is the technology behind cool stuff like virtual assistants (like Siri or Alexa) and language translation tools. These devices make life simpler and more enjoyable, like having a helpful friend who can speak every language.

Being Fair and JustSometimes, people can make unfair decisions because of their biases. Machine learning can be set up to be fair and impartial. It helps make choices in things like hiring or lending money based on facts, not feelings.

Driving DiscoveriesMachine learning isn't only for businesses; it also helps scientists. They use it to study complex data and make discoveries in areas like genetics, space exploration, and materials science. It's like having a super microscope that shows things we couldn't see before.

Protecting Our PlanetMachine learning can also help us take care of the environment. It's used to keep an eye on pollution, track changes in the climate, and predict natural disasters. By studying a lot of data, we can make better decisions to protect the Earth.

Improving HealthcareHealthcare is getting a boost from machine learning. It helps doctors diagnose diseases, discover new medicines, and keep track of patients. It's like having a super-smart medical assistant.

In summary, machine learning is a superpower that's changing the world. It makes handling data easy, automates tasks, personalizes experiences, and solves tough problems. It's fair, it's quick, and it's shaping the future. With machine learning, the possibilities are endless. If you want to learn more about machine learning or need help using it in your business, you can ask experts or take special courses. ACTE Institute offers courses that can give you the knowledge and skills to do well in this field. Think about all the amazing chances for analysing data, automating tasks, and making decisions that machine learning offers.

[prettymunchkin]

Unlocking the Future: AI's Hidden Potential in Real Estate

In an era characterized by rapid technological advancements, industries across the board are witnessing transformative changes, and the real estate sector is no exception. As AI continues to permeate various aspects of our lives, its potential to empower the real estate industry remains largely untapped. In this blog post, we'll explore the unique ways in which AI can revolutionize the real estate landscape, unlocking new opportunities and reshaping traditional practices.

Democratizing Access to Real Estate Investment: Traditionally, investing in real estate has been perceived as exclusive to those with substantial capital and industry expertise. However, AI-powered platforms are democratizing access to real estate investment by offering fractional ownership and crowdfunding opportunities. Through algorithms that analyze market trends and assess investment risks, these platforms enable individuals to diversify their portfolios and participate in lucrative real estate ventures with lower barriers to entry.

Sustainable Development and Green Building Initiatives: As sustainability becomes a key priority in urban development, AI can play a pivotal role in advancing green building initiatives. Machine learning algorithms can optimize building designs for energy efficiency, leverage IoT sensors to monitor resource consumption, and forecast environmental impacts. By integrating AI-driven sustainability solutions into real estate projects, developers can minimize carbon footprints, reduce operational costs, and create healthier living environments for occupants.

Enhancing Urban Planning and Smart Cities: AI's ability to process vast amounts of data can revolutionize urban planning and contribute to the realization of smart cities. By analyzing demographic trends, traffic patterns, and infrastructure requirements, AI algorithms can optimize land use, improve transportation networks, and enhance urban resilience. Through predictive modeling and scenario analysis, city planners can make data-driven decisions that foster sustainable growth, mitigate congestion, and enhance quality of life for residents.

Empowering Real Estate Agents with Intelligent Assistants: Real estate agents juggle numerous tasks, from lead generation to property management, often leading to time constraints and inefficiencies. AI-powered virtual assistants can alleviate this burden by automating repetitive tasks, such as scheduling appointments, responding to inquiries, and generating personalized property recommendations. By leveraging natural language processing and sentiment analysis, these intelligent assistants can enhance customer engagement, streamline workflows, and enable agents to focus on high-value activities.

Predictive Analytics for Property Development: AI-driven predictive analytics offer valuable insights into future property trends and demand dynamics, guiding developers in making informed decisions throughout the development lifecycle. By analyzing factors such as population growth, economic indicators, and consumer preferences, AI algorithms can identify prime locations for new developments, optimize property designs, and forecast market demand with greater accuracy. This proactive approach minimizes investment risks and maximizes returns on real estate projects.

Cultural Preservation and Heritage Conservation: Preserving cultural heritage sites and historic buildings is crucial for maintaining a sense of identity and preserving collective memory. AI technologies, such as computer vision and image recognition, can aid in the documentation, restoration, and conservation of cultural landmarks. By digitizing architectural artifacts, analyzing structural integrity, and simulating restoration scenarios, AI contributes to the preservation of cultural heritage for future generations to appreciate and cherish.

Conclusion:

As we navigate the complexities of an ever-evolving real estate landscape, embracing AI's transformative potential is key to unlocking new opportunities and addressing emerging challenges. From democratizing access to investment opportunities to advancing sustainable development initiatives, AI empowers stakeholders across the real estate industry to innovate, adapt, and thrive in a rapidly changing world. By harnessing AI's capabilities, we can build more resilient, inclusive, and sustainable communities for generations to come.