#Line Scan Camera Market Analysis

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Line Scan Camera Market is Poised to Grow at a CAGR of 7.7% during the Forecast Period of 2029 | Teledyne Technologies, Basler AG (Basler), Cognex Corporation, VIEWORKS Co.

Line Scan Camera Market report is a consolidation of primary and secondary research, which provides market size, share, dynamics, and forecast for various segments and sub-segments considering the macro and micro environmental factors. It also gauges the bargaining power of suppliers and buyers, threat from new entrants and product substitutes, and the degree of competition prevailing in the market.

Line Scan Camera market research report contains a unique mix of tangible insights and qualitative analysis to help companies achieve sustainable growth. Our experienced analysts and consultants use industry-leading research tools and techniques to compile comprehensive market studies interspersed with relevant data. Furthermore, the report offers an up-to-date analysis of the current market scenario, the latest trends and drivers, and the overall market environment. It also examines market performance and the position of the market during the forecast period.

Line Scan Camera Market will exhibit a CAGR of 7.7% in the forecast period of 2019 To  2029 and is expected to reach above USD 1.6 billion by  2029.

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Top Leading Companies of Global Line Scan Camera Market are Teledyne Technologies, Basler AG (Basler), Cognex Corporation, VIEWORKS Co., Ltd., JAI A/S, Nippon Electro-Sensory Devices, Chromasens GmbH, IDS Imaging Development Systems, Photonfocus, Allied Vision Technologies GmbH, Xenics etc and others.

Market Segmentation:

The regions are further sub-divided into:

-North America (NA) – US, Canada, and Mexico -Europe (EU) – UK, Germany, France, Italy, Russia, Spain & Rest of Europe -Asia-Pacific (APAC) – China, India, Japan, South Korea, Australia & Rest of APAC -Latin America (LA) – Brazil, Argentina, Peru, Chile & Rest of Latin America -Middle East and Africa (MEA) – Saudi Arabia, UAE, Israel, South Africa

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Impact of the Line Scan Camera Market report:

–Comprehensive assessment of all opportunities and risk within the Line Scan Camera Market.

–Line Scan Camera Market recent innovations and major events.

–Detailed study of business ways for growth of the Line Scan Camera Market market-leading players.

–Conclusive study concerning the expansion plot of Line Scan Camera market place for forthcoming years.

–In-depth understanding of Market drivers, constraints and major small markets.

–Favourable impression within important technological and market latest trends placing the Market.

Key Reasons to Purchase Line Scan Camera Market Report

·         The research examines the size of the global market overall as well as potential prospects across a number of market segments.

·         With the accurate information and useful tactics in the research report, market participants have expanded their businesses and clientele.

This Report Also Includes:

·         Exactitude Consultancy Methodology

·         Tactics and Suggestions for New Entrants

·         Segmentation Analysis

·         Economic Indices

·         Companies Strategic Developments

·         Market Growth Drivers and Restraints

·         Selected Illustrations of The Market Penetrations and Trends

Table of Contents

1.       Line Scan Camera Market Definition & Scope

2.       Line Scan Camera Market Development Performance under COVID-19

3.       Industrial Lift Cycle and Main Buyers Analysis

4.       Line Scan Camera Market Segment: by Type

5.       Line Scan Camera Market Segment: by Application

6.       Line Scan Camera Market Segment: by Region

7.       North America

8.       Europe

9.       Asia Pacific

10.   South America

11.   Middle East and Africa

12.   Key Participants Company Information

13.   Global Line Scan Camera Market Forecast by Region by Type and by Application

14.   Analyst Views and Conclusions

15.   Methodology and Data Source

About Exactitude Consultancy

Exactitude Consultancy is a market research & consulting services firm which helps its client to address their most pressing strategic and business challenges. Our market research helps clients to address critical business challenges and also helps make optimized business decisions with our fact-based research insights, market intelligence, and accurate data. Contact us for your special interest research needs at sales@exactitudeconsultancy.com and we will get in touch with you within 24hrs and help you find the market research report you need.

Website: https://exactitudeconsultancy.com/

Irfan Tamboli

Contact: +91-7507-07-8687

[stevecarell600]

Sonar System Market Size, Share, Industry Growth, Trends, and Segment Analysis by 2032

The SONAR system market size is predicted to reach USD 3.76 billion by 2029 and exhibit a CAGR of 7.96% during the projected period. Fortune Business InsightsTM has presented this information in its report titled, “SONAR System Market, 2022-2029”. The market stood at USD 2.09 billion in 2021 and USD 2.20 billion in 2022. Sound Navigation and Ranging (SONAR) is a sophisticated technique that uses sound propagation to navigate and communicate with underwater objects.

Informational Source:

The use of SONAR systems with deep neural networks is revolutionizing fish monitoring, especially in aquaculture farms where expanding fish resources is a priority. These systems combine high-precision imaging SONAR with advanced underwater optical cameras, enabling clear monitoring even at night. This technology is improving the efficiency of fish farming and driving demand in the market.

Traditional optical cameras struggle to capture images in low light or murky water, making night monitoring challenging. However, advancements in underwater optical cameras that work seamlessly with SONAR systems are boosting market growth. For example, the SCAN-650 sector scanning SONAR, developed by JW Fishers, is widely used globally. It delivers detailed images of underwater environments, regardless of water clarity, enhancing fish monitoring capabilities.

List of Key Market Players:

ASELSAN A.Ş. (Turkey)

ATLAS ELEKTRONIK INDIA Pvt. Ltd. (India)

DSIT Solutions Ltd. (Israel)

EdgeTech (U.S.)

FURUNO ELECTRIC CO., LTD. (Japan)

Japan Radio Co. (Japan)

KONGSBERG (Norway)

Lockheed Martin Corporation (U.S.)

L3Harris Technologies, Inc. (U.S.)

NAVICO (Norway)

Raytheon Technologies Corporation (U.S.)

SONARDYNE (U.K)

Teledyne Technologies Incorporated. (U.S.)

Thales Group (France)

Ultra (U.K)

The SONAR systems market is highly competitive, with many companies contributing to its development. Key trends in the market include surveillance network SONAR, diver detection systems, dual-axis SONAR (DAS), and chirp technology. Leading players dominate due to their diverse product offerings and strong focus on research and development. For instance, in March 2020, Impact Subsea introduced the ISS360 SONAR, the world’s smallest imaging SONAR. It delivers high-quality images with a range of up to 90 meters (295 feet).

Teledyne Technologies Incorporated stands out by offering a wide range of 2D and 3D SONARs, acoustic modems, and data visualization/charting software. Their technology is designed to accommodate all types of sound navigation systems for naval vessels.

Segments:

On the basis of product type, the market is divided into sonobuoy, stern-mounted, hull-mounted, and DDS. On the basis of application, the market is split into defense and commercial. On the basis of platform, the market is divided into airborne and ship type. On the basis of solution, the market is divided into hardware (control units, transmitter and receiver, displays sensors, which is further divided into ultrasonic diffuse proximity sensors, VME-ADC, ultrasonic through-beam sensors, ultrasonic retro-reflective sensors, and others), and software. On the basis of end-user, the market is bifurcated into retrofit and line fit. Geographically, the market is classified into Europe, North America, Asia Pacific, and the Rest of the World.

Report Coverage:

The research report provides a thorough examination of the market. It focuses on key aspects such as leading companies, various platforms, product types, solutions, and SONAR system applications. Apart from that, the report provides insights into market trends and highlights important industry developments. In addition to the aforementioned factors, the report includes a number of factors that have contributed to the development of the developed market in recent years.

Drivers & Restraints:

Tactical Defense Operations are Surging the Demand for Sonobuoys

A sonobuoy is a sophisticated underwater acoustic research system that naval ships drop or eject. Sonobuoys use a sophisticated transducer and a radio transmitter to record and transmit underwater sounds. Other environmental data, such as wave height and water temperature, are also provided by special-purpose buoys. The market is expected to expand as the use of sonobuoys in military vessels expands. However, the steep cost associated with SONAR development may impede the SONAR system market growth.

Regional Insights:

North America to be a Dominant Region of the Global Market

North America dominated the market in 2021, with market size of USD 665.5 million. North America's dominance is owing to the rise in naval shipbuilding in the U.S. 82 new ships costing up to USD 147 billion will be added in the U.S. between 2022 - 2026, according to a shipbuilding plan announced in 2020.

Asia Pacific will experience remarkable growth as a result of increased naval spending and an increase in domestic ship manufacturing in China and South Korea. Ship deliveries in Japan have grown and various South Korean shipbuilding players have integrated automation into ship systems to drive the market development.

As the SONAR system market share increases in Europe, this is largely driven by the introduction of a new generation of threat detection and identification capabilities in ships and the retrofitting of vessels with autonomous engineering systems. Increased investment in marine system upgrades is anticipated to fuel the market in the U.K.

Competitive Landscape:

The dominant factor responsible for these key market players' dominance is a diverse product portfolio combined with R&D activity. Impact Subsea will launch the ISS360 SONAR, the world's tiniest imaging SONAR, in March 2020. It has a capacity of up to 90 meters/295 feet and provides excellent image quality.

Key Industry Development:

February 2022: Leonardo SpA awarded ELAC SONAR a USD 58 million contract to supply SONAR systems for two new submarines supplied by Fincantieri for the Italian Navy.

[batch-freight]

NYSE Companies Lead the Charge: How Tech and AI Are Changing the Game

Picture this: you wake up to a home that adjusts the thermostat based on the weather forecast, your favorite coffee blend is already brewing, and your morning news is curated to your interests—all powered by Artificial Intelligence (AI). Now, scale that innovation to industries, and you’ll understand how NYSE-listed companies are leveraging AI to reshape the way we live and work.

From healthcare breakthroughs to retail revolutions, AI isn’t just a tool—it’s a force driving a new era of possibilities. Let’s dive into how this technology is making waves across industries, spearheaded by some of the world’s most innovative firms on the New York Stock Exchange.

The Financial Frontier: Smarter, Faster, Safer

AI has transformed the financial world into a realm of precision and foresight. Gone are the days of gut-feel investments; now, algorithms process mountains of data in seconds, identifying trends that were once invisible to the human eye.

NYSE-listed financial giants are using AI for:

Real-Time Market Analysis: Imagine having an assistant who predicts market changes before they happen. That’s the power AI gives to investors.

Fraud Detection: AI’s sharp eye can spot irregularities faster than any human auditor, safeguarding billions of dollars.

Personalized Banking: Think virtual advisors that know your spending habits and help you save better—they’re redefining how we manage money.

AI in finance isn’t just about profits; it’s about creating smarter, more secure systems for everyone.

Healthcare Reimagined: AI as the Ultimate Healer

What if a doctor could diagnose diseases faster and more accurately than ever before? That’s the reality AI is bringing to healthcare. NYSE-listed biotech firms are at the forefront, turning science fiction into science fact:

Drug Discovery: AI analyzes millions of molecular combinations to find potential treatments, cutting years off the development process.

Early Diagnosis: AI tools detect anomalies in medical scans with astonishing precision, often spotting diseases before symptoms appear.

Patient-Centered Care: From chatbots answering health queries to apps monitoring vitals, AI ensures patients feel cared for around the clock.

In the hands of healthcare innovators, AI isn’t just a tool—it’s a lifeline.

Retail Revolution: Shopping, Smarter and Simpler

Ever had a shopping site suggest the exact product you were thinking of? That’s AI working its magic. Retailers on the NYSE are creating personalized experiences that make every customer feel like a VIP.

Hyper-Personalization: AI learns what you love and recommends products you didn’t even know you wanted.

Efficient Logistics: AI-powered systems predict demand, ensuring shelves are always stocked without waste.

Seamless Shopping: Whether it’s a cashier-less store or same-day delivery, AI is making shopping faster and more convenient.

For retailers, AI isn’t just about selling—it’s about connecting with customers in meaningful ways.

Manufacturing Gets an AI Makeover

Factories aren’t just places of labor anymore—they’re hubs of innovation. With AI, NYSE-listed manufacturing firms are turning ordinary assembly lines into intelligent production powerhouses.

Predictive Maintenance: Machines now tell us when they need repairs, reducing costly downtime.

Mass Customization: Whether it’s cars or sneakers, AI makes personalized products at scale a reality.

Quality Assurance: AI-powered cameras catch defects that human eyes might miss, ensuring top-notch products every time.

In manufacturing, AI isn’t replacing workers; it’s empowering them to do more with less effort.

The Green Revolution: AI Powers Sustainability

AI is helping NYSE-listed energy companies tackle one of humanity’s biggest challenges: sustainability. From smarter grids to greener solutions, AI is making clean energy smarter and more accessible.

Optimizing Renewables: AI predicts when the sun will shine or the wind will blow, maximizing the efficiency of solar and wind farms.

Energy Savings: Smart systems analyze energy consumption patterns, helping consumers and businesses reduce their carbon footprints.

Preventative Maintenance: By identifying weak spots in power grids, AI prevents outages and keeps the lights on.

For the energy sector, AI isn’t just about profits—it’s about building a better planet.

The Balancing Act: Ethics in AI

As exciting as AI’s possibilities are, they come with challenges that NYSE-listed companies are addressing head-on:

Bias: Ensuring AI treats all users fairly is a top priority.

Privacy: Protecting user data is critical for trust and compliance.

Job Impact: With automation on the rise, retraining workers is key to fostering inclusive growth.

Ethical AI is more than a buzzword—it’s the foundation for sustainable innovation.

The Road Ahead: AI and the NYSE’s Bright Future

The companies leading the AI charge on the NYSE aren’t just shaping industries—they’re shaping the future. With advancements in generative AI, autonomous systems, and even quantum computing on the horizon, the possibilities are limitless.

For businesses, the takeaway is clear: adopting AI isn’t optional; it’s essential for staying competitive. For individuals, understanding AI’s impact is the first step in thriving in this new era of innovation.

Welcome to the Age of AI

AI is not just a technological tool—it’s a catalyst for change, a driver of progress, and an enabler of dreams. As NYSE-listed companies continue to push boundaries, we’re witnessing a revolution that’s transforming how industries operate and how we live our lives.

Whether it’s helping a doctor save a life, an investor make a smart move, or a shopper find the perfect item, AI is weaving itself into the fabric of our everyday experiences. The best part? This is just the beginning.

So, buckle up. The AI-powered future is here, and it’s going to be extraordinary.

[semiconductorsandelectronics]

Shortwave Infrared (SWIR) Market Size, Share & Industry Trends Growth Analysis Report by Camera, Lenses, Spectral Imaging, Area & Line Scan, Active & Passive Thermal Imaging, Pushbroom, Snapshot, Security & Surveillance, Monitoring & Inspection, Technology, Vertical and Region – Global Forecast to 2029

[teamrushipandit]

Keyword Research Tactics: How Long Tail Keywords Can Transform Your SEO

Keyword research is the foundation of a successful search engine optimization strategy. If used correctly, it optimizes websites to higher ranking positions on SERPs, drives organic traffic to the website, and reaches the target audience. Among the elements involved in this process is long-tail keyword usage: more specific phrases that attempt to hit niche audiences with intent-driven traffic.

In this blog, we’ll uncover what long tail keywords are, their significance in SEO, and actionable tips to discover them for better rankings.

What is a Long Tail Keyword?

Long tail keywords are specific, multi-word phrases. In keyword research they are about specific search intent and more different from broad keywords, which may have a relatively lower search volume but are easier to rank for just because of the minimal competition.

Examples:

Broad keyword: “Shoes”

Long-tail keyword: “Women running shoes with arch support”

Benefits of Using Long Tail Keywords for SEO

Incorporation of long tail keywords in the keyword research benefits in the following manners:

1. Less Competition:

Because long tail keywords are not so competitive as compared to broad, high-volume keywords, it can easily rank higher for small or new sites.

2. Higher Conversion Rates:

Using long tail keywords tends to attract users having clear buying intent. For instance, a person searching for “affordable digital marketing courses in Mumbai” has reached closer to the moment of decision than the person who searches for “digital marketing.”.

3. Improved User Experience:

While aligning your content to the intent of the users, you end up improving click-through rates, quality engagement, and lowering the bounce rate.

4. Voice Search Optimized

Why Are Long Tail Keywords Important?

Long tail keywords play a very significant role in your keyword research and your entire SEO strategy:

They are closer to a user’s intent. These often closely align with the kind of users who are ready to make a purchase or seeking very specific information.

Example:

Broad: “Camera”

Long Tail: “Best DSLR camera for beginners under $500”

Integrated SEO Strategy: Optimization for multiple long tail keywords can collectively drive high amounts of traffic, helping websites rank for many niche terms.

How to Find Long Tail Keywords for SEO

Here are 10 tested ways to find long-tail keywords for your keyword research projects:

1. Google Suggest:

Input a general keyword into Google’s search bar and click through the drop-down suggestions. These are organic searches by users and very much a valid source of long-tail keywords.

2. People Also Ask (PAA):

Google PAA is the related questions users find on Google. Use them to come up with content ideas and long-tail keyword potentialities.

3. Keyword Research Tools:

Long tail keywords can be found on platforms like Ahrefs, SEMrush, and Ubersuggest, by showing search volumes, competition levels, and related phrases.

4. Competitor Analysis:

Tools such as SEMrush or Moz can scan competitor websites and then find the untapped long tail keywords which they rank for.

5. Forums and Q&A Sites:

6. Google Analytics:

Go through the search queries that are taking users to your site. The long-tail keywords already working for you can inspire similar optimizations.

7. Long Tail Keyword Generators:

Use tools such as AnswerThePublic that allow for visualizing questions people are constantly asking-thus long list ideas for keywords in the long tail.

8. Social Media Insights:

Trend lines and trending hashtags on Instagram and Twitter can help identify long-tail keywords.

9. Local SEO Optimization:

Geographically-oriented keywords should be used during local optimization, for example “best digital marketing agency in New York”.

10. Monitor Trends and Seasonality:

Summary

No SEO keyword research strategy is ever completely complete without long tail keywords. Such phrases are often targeted to specific niches of audiences and cut competition, conversion rates. By putting the above strategies in place, you will be finding the perfect long tail keywords to help your SEO efforts jump to new heights in rankings, traffic, and eventually success.

Bring out the full potential for your website using long tail keywords today!

For more information, Visit now

For more blogs — rushipandit.com

[nv5geospatial]

Mobile Mapping Explained

Mobile mapping is a technique used to survey infrastructure through the use of vehicles rather than boots-on-the-ground efforts. 

These vehicles, including automobiles, drones, and boats, are equipped with various sensors, including LiDAR technology, cameras, and GPS receivers. The sensors rapidly collect detailed 3D data of the environment as the vehicle moves. 

The result is an accurate 3D model of the surroundings, which can be used for a wide variety of applications in transportation, urban planning, and infrastructure management.

It’s not only more accurate than on-the-ground surveys but it is safer and less disruptive.

How Mobile Mapping Works

The core technology behind mobile mapping is LiDAR (Light Detection and Ranging), which uses laser pulses to measure distances between the sensor and surrounding objects. 

The data collected creates a "point cloud," representing the scanned environment in 3D. 

Alongside LiDAR, high-resolution cameras capture imagery, which can be integrated with the LiDAR data to enhance its visualization. 

The vehicle also uses GPS and sensors called inertial measurement units to ensure data accuracy even while moving or encountering bumps in the road. 

The mobile mapping process typically follows these steps:

Data Collection: A vehicle equipped with LiDAR sensors, cameras, and GPS systems captures detailed data on roads, buildings, and other infrastructure as it moves along the planned route.

Data Processing: Specialized software processes the raw data, aligning and filtering it to create accurate and usable geospatial information. Algorithms integrate the different datasets, ensuring accuracy and consistency.

Analysis and Visualization: The data is analyzed using tools that can extract meaningful insights, such as identifying structural issues in roads or bridges. It is then visualized through interactive 3D models or maps for easier interpretation and decision-making​.

Applications in Transportation Projects

Mobile mapping is highly suited for various transportation infrastructure projects due to its accuracy and efficiency:

Roadway and Rail Network Mapping: This technique maps road surfaces, rail lines, and surrounding infrastructure, such as bridges and signage. The data generated supports road design, maintenance, and expansion projects.

Bridge and Tunnel Inspection: Mobile mapping is ideal for detecting structural issues, such as cracks and deformations, without disrupting traffic​, because it can capture data under bridges and tunnels.

Right-of-Way (ROW) Surveys: Detailed mapping of road corridors allows transportation agencies to manage their right-of-way assets efficiently, making it easier to plan for expansions or repairs​.

Accuracy of Mobile Mapping

Mobile mapping achieves impressive accuracy down to just centimeters.  

The accuracy depends on the quality of the sensors used, the speed of the data acquisition, and the environmental conditions. 

Compared to airborne LiDAR, mobile mapping typically provides higher-resolution data since the sensors are closer to the ground​.

Mobile Mapping vs. Traditional Surveying Methods

Mobile mapping offers several advantages over traditional surveying:

Speed: It collects data much faster than manual methods, which require surveyors to walk the project area, often over multiple days. With mobile mapping, large areas can be scanned in a fraction of the time, sometimes within hours.

Safety: By eliminating the need for surveyors to physically access dangerous or high-traffic areas, mobile mapping enhances safety for workers.

Data Detail: Mobile mapping captures significantly more data than manual surveys, providing a complete 3D model of the environment, rather than just individual points of interest​

Mobile mapping first started gaining popularity in the 1980s, and it is still growing — now projected to be a sector of the market worth $105 billion by 2029.

Using Mobile Mapping Data

Once collected, the data from mobile mapping can be used in numerous ways:

3D Modeling: Engineers use the detailed 3D models for designing transportation infrastructure, including roads, railways, and bridges.

Asset Management: Transportation departments use the data to manage and monitor infrastructure assets, from traffic signs to utilities.

Maintenance Planning: The collected data supports proactive maintenance by identifying issues such as pavement cracks, surface deformations, or vegetation encroachments, enabling timely repairs​.

In conclusion, mobile mapping is a highly effective and efficient tool for collecting geospatial data, particularly for transportation projects. 

Its ability to capture detailed, high-accuracy data quickly and safely makes it a superior choice over traditional surveying methods, especially in complex environments like roadways and rail networks. 

As technology continues to evolve, mobile mapping will become increasingly important in infrastructure development and maintenance.

[fettesans]

Left, screen capture from the X account of Akshay Kothari showing a handwritten boarding pass, following the worldwide IT outage caused by a software update currently disrupting air travel, health care and shipping, July 19, 2024. Via. Right, Dennis Oppenheim, Rocked Circle – Fear, 1971, from a portfolio of 10 lithographs based on Super 8 film loop of 30 min. Text reads:

A situation was created which allowed registration of an exterior stimulus directly through facial expression. As I stood in a 5’ diameter circle, rocks were thrown at me.  The circle demarcated the line of fire. A video camera was focused on my face. The face was captive, it’s expression a direct  result of the apprehension of hazard. Here, stimulus is not abstracted from it’s source. fear is the emotion which produced a final series of expressions on the face. Via.

--

There’s something disconcerting about a sophisticated piece of surveillance technology deployed for something as banal as selling candy. Invenda, the Swiss company behind the machines, issued a statement to the CBC that no cameras were inside the machines and the software was designed for facial analysis, not recognition—it was there to “determine if an anonymous individual faces the device, for what duration, and approximates basic demographic attributes unidentifiably.” (The CBC report pointed out that Invenda’s CEO had used the term “facial recognition” in a previous promo video.) The Waterloo Reddit thread opened up a can of other questions. Why would a company collect this information to begin with? How many of the mundane transactions that make up our daily lives are being mined for intimate biometric data without our knowledge or approval? And how far will this technology go? (...)

The UK is so taken with facial recognition that, in April, the government announced plans to fund vans that could scan people walking along high streets—in other words, public spaces—in search of shoplifters. Last March, CBS reported that Fairway supermarket locations in New York City had started using biometric surveillance to deter theft, alerting shoppers with a sign in store entrances that the chain is harvesting data such as eye scans and voice prints. Some customers hadn’t realized they were being watched. One told the outlet: “I noticed the cheese sign and the grapes, but not the surveillance, yeah.” Last year, New York Times reporter Kashmir Hill found facial recognition software in use at a Manhattan Macy’s department store as well as at Madison Square Garden. She tried to accompany a lawyer involved in litigation against the arena’s parent company to a Rangers-versus-Canucks game there. The lawyer was promptly recognized by the tech, security was alerted, and a guard kicked her out.

It’s somewhat ironic that large corporations, seemingly concerned with theft, are taking the biometric data of individuals without their knowing. But facial recognition and analysis provide another perk to companies: opportunities to amass more data about us and sell us more stuff. For example, FaceMe Security, a software package patented by CyberLink, promises retailers intel through a face-scanning security and payment system. It can provide data on who is buying what and when, insights into a customer’s mood during the purchasing process, as well as age and gender information—to better time potential sales pitches and other demographic-specific marketing possibilities.

Monika Warzecha, from Facial Recognition at Checkout: Convenient or Creepy? - I don’t want to pay for things with my face, for The Walrus, July 5, 2024.

See also, Blue Screen of Death.

[jcmarchi]

6 Ways Computer Vision is Re-envisioning the Future of Driving

New Post has been published on https://thedigitalinsider.com/6-ways-computer-vision-is-re-envisioning-the-future-of-driving/

6 Ways Computer Vision is Re-envisioning the Future of Driving

Today’s cars are like supercomputers on wheels – smarter, safer, faster, and more personalized thanks to technological advances.

One transformative innovation steering this revolution is computer vision – AI-driven technology that enables machines to “understand” and react to visual information. Vehicles can now identify the specific attributes of objects, text, movement, and more – critically important for an industry in pursuit of self-driving vehicles.

Here are 6 ways computer vision is driving cars into the future.

Driver Assistance & Behavior Analysis

Advanced Driver Assistance Systems (ADAS) – a computer vision-powered “third eye” that alerts drivers to potential dangers or hazards – are already a feature of most new cars on the road today.

Using cameras placed throughout the body of a vehicle, ADAS continually monitors a car’s surroundings, alerting drivers to hazards they might otherwise miss. This enables features such as lane departure warnings, blind spot detection, collision avoidance, pedestrian detection, and even parking assistance.

These cameras can also monitor the in-car environment, detecting if drivers are distracted, drowsy, have their hands off the wheel, or are checking their phones. If such systems register risky behavior, they can alert the driver, recommend pulling over for coffee or a nap, or even take control of the car to prevent an accident.

ADAS technologies could save about 20,841 lives each year, preventing around 62% of all traffic-related deaths. With their promise of safer roads, the global market for ADAS is set to increase to $63 billion by 2030, up from $30 billion this year.

Autonomous Driving

Autonomous driving is the dream fuelling automotive innovation today – and computer vision is a critical flagstone on the path to fully self-driving vehicles.

By 2030, an estimated 12% of new passenger cars will have L3+ autonomous technologies, which allow vehicles to handle most driving tasks. 5 years after that, 37% percent of cars will have advanced autonomous driving technologies.

Computer vision technologies empower autonomous vehicles to mimic the human ability to perceive and interpret visual information and respond as safely as possible. Computer vision systems enable AV capabilities by analyzing the road in real-time while identifying and reacting to visual data such as pedestrians, vehicles, traffic signs, and lane markings. Paired with machine learning algorithms which enable the system to continuously improve its recognition capabilities through experience and exposure to the data it is constantly accumulating, computer vision allows for better decision-making in complex driving scenarios.

Automated Assembly and Quality Control:

Even before cars hit the road, the integration of computer vision in automotive assembly lines has significantly enhanced quality control processes.

Computer vision can automatically and accurately inspect every part of the car at every stage, from paintwork to screws to electronics to welding. Companies like BMW have already infused computer vision into their manufacturing process to great effect.

By using computer vision to inspect vehicles during assembly, manufacturers ensure that everything meets the highest standards, significantly increasing speed and safety and cutting down on scrapped vehicles, dangerous flaws, and expensive recalls.

Vehicle Inspection and Maintenance

Traditional manual vehicle inspections tend to be time-consuming and prone to human error. Computer vision can automate the inspection process – scanning vehicles with new precision, granularity, and efficiency to accurately identify any issues that need fixing like tire conditions, dents, scratches, and damaged or worn-out parts.

This benefits not only drivers and repair shops, but dealerships and fleet management operations as well.

By automating inspection and maintenance processes, dealerships can ensure that every vehicle meets quality standards before reaching customers, assuring buyers that they aren’t being taken for a ride. Additionally, regular maintenance and inspections are also essential to keeping commercial fleets operational and minimizing downtime.

Smart Cities and Traffic Management

Efficient traffic management is crucial for ensuring smooth transportation flow and keeping cities safer and cleaner. Computer vision systems can empower smart cities to optimize their traffic management, minimizing congestion, and reducing commute times, accidents, and pollution.

Computer vision sensors collect vast amounts of real-time data on the volume, flow, and direction of traffic in any given area, which is used to optimize traffic lights, among other things. Unlike traditional fixed-time traffic lights, dynamic traffic light optimization adjusts signals in real-time based on current traffic conditions, ensuring a much smoother flow on the roads.

License Plate Recognition

Many drivers don’t realize that they already encounter computer vision whenever they drive through an automated toll booth.

These systems can instantaneously read a car’s license plate number, even at high speeds, enabling automatic toll collection, as well as parking lot management and traffic regulation. It can also be used for security and enforcement – for example, tracking the license plate of a stolen car, enforcing traffic rules by putting out alerts on reckless drivers, or automatically ticketing speeders, keeping roads safer and helping drivers to be more cautious.

Eyes on the Prize

Computer vision is already making cars safer, more efficient, and smarter. From enhancing safety and improving manufacturing, to optimizing traffic flow and paving the road towards autonomous driving, this technology is putting the way we move into overdrive.

The continued evolution of computer vision brings us closer to a future where driving is better in every sense. Drivers and manufacturers alike should be eager to see what awaits from this dazzling technology not so far down the road.

[priyablog26]

Global Machine Vision Market Research and Analysis by Expert: Cost Structures, Growth rate, Market Statistics and Forecasts to 2030

Global Machine Vision Market Size, Share, Trend, Growth and Global Opportunity Analysis and Industry Forecast, 2023-2030.

Overview

The Global Machine Vision Market  is likely to exhibit steady growth over the forecast period, according to the latest report on Qualiket Research.

Global Machine Vision Market was valued at USD 14.4 billion in 2022 and is slated to reach USD 27.86 billion by 2030 at a CAGR of 8.60% from 2023-2030.

Machine vision (MV) is a field of computer science that focuses on providing imaging-based automatic inspection and analysis for a variety of industrial applications, including process control, robot guiding, and automatic inspection.

Key Players:

Allied Vision Technologies GmbH

Basler AG, Cognex Corporation

 Keyence Corporation

LMI Technologies, Inc.

 Microscan Systems, Inc.

 National Instruments Corporation

 OMRON Corporation

 Sick AG

 Tordivel AS.

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Market Segmentation

Global Machine Vision Market is segmented into By Type, Component, Function Module, Platform, Camera Vision & Lenses and Industry. By Type such as 1D Vision Systems, 2D Vision Systems, Area Scan, Line Scan, 3D Vision Systems. By Components such as Hardware, Software, Services. By Function Module such as Positioning/, Guidance/ Location, Identification, Inspection and Verification, Gauging/ Measurement, Soldering and Welding, Material Handling, Assembling and Disassembling, Painting and Dispensing, Others) By Platform such as PC Based, Camera Based Vision System. By Camera Vision and Lenses such as Lens, Telecentric Lenses, Macro and Fixed Focal Lenses, 360-degree view lenses, Infrared & UV lenses, Short Wave Infrared Lenses, Medium Wave Infrared Lenses, Long Wave Infrared Lenses, Ultraviolet Lenses, Camera Vision, Area Scan Camera, Line Scan Cameras). By Industry such as Industrial Applications, Automotive, Electronics Manufacturing, Food & Beverage Manufacturing, Packaging, Semiconductors, Pharmaceuticals, Warehouse & Logistics, Wood & Paper, Textiles, Glass, Rubber & Plastic, Non-Industrial Applications, Printing, Sports & Entertainment.

Regional Analysis

Global Machine Vision Market is segmented into five regions Americas, Europe, Asia-Pacific, and the Middle East & Africa. A high number of providers with local roots are present in North America, which has the greatest market for machine vision because of the region's early adoption of manufacturing automation. Its supremacy in this market is also a result of the semiconductor sector's dominance in the North American region, a crucial sector for MV systems. Europe is the second-largest market for machine vision, thanks to a robust industrial sector and rising automation demand. Some of the top machine vision manufacturers and suppliers are based in this area.

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Market Growth, Size, Market Segmentation and Future Forecasts to 2030

Overview

Latest published report on the Machine Vision  market, found on the Qualiket Research website revealed a great deal about various market dynamics. These driving factors influence the market from a very miniscule level to its holistic standard and can traverse limitations to assist the market achieve an significant growth rate over the analysis period of 20Machine Vision -20Machine Vision . The report is based on an extensive study supervised by adept analysts. Their sound knowledge and expertise in the field help in unearthing of factors and figures. The report is fulfilled with a volume-wise and value-wise analysis. This type of analysis offers a better outlook regarding the movement of the market and potential of the market.

For a better understanding of the Machine Vision  market, a better grip over the macroeconomic and microeconomic factors are needed as they are impacting the market towards progress. Those factors can ensure a swift helming of the market by rough patches of economic crisis and help in averting plummeting results. With real-time data, the report captures the essence of the market and provides a close reading of demographic changes. Report would assist key players in assessing growth opportunities and optimally use resources provided by growth pockets.

However, the fragmented Machine Vision  market has several new entrants that are giving tough competition to the established names. As a result, the Machine Vision  market is opening up and is becoming active with new merger, acquisition, product launch, collaboration, innovation, and other methods. At the same time, these tactical moves depend a lot on their geographical location as the demography facilitates moves. A detail inspection of these regions has been included to simplify demographic understanding.

Market Segmentation

Global Machine Vision Market is segmented into By Type, Component, Function Module, Platform, Camera Vision & Lenses and Industry. By Type such as 1D Vision Systems, 2D Vision Systems, Area Scan, Line Scan, 3D Vision Systems. By Components such as Hardware, Software, Services. By Function Module such as Positioning/, Guidance/ Location, Identification, Inspection and Verification, Gauging/ Measurement, Soldering and Welding, Material Handling, Assembling and Disassembling, Painting and Dispensing, Others) By Platform such as PC Based, Camera Based Vision System. By Camera Vision and Lenses such as Lens, Telecentric Lenses, Macro and Fixed Focal Lenses, 360-degree view lenses, Infrared & UV lenses, Short Wave Infrared Lenses, Medium Wave Infrared Lenses, Long Wave Infrared Lenses, Ultraviolet Lenses, Camera Vision, Area Scan Camera, Line Scan Cameras). By Industry such as Industrial Applications, Automotive, Electronics Manufacturing, Food & Beverage Manufacturing, Packaging, Semiconductors, Pharmaceuticals, Warehouse & Logistics, Wood & Paper, Textiles, Glass, Rubber & Plastic, Non-Industrial Applications, Printing, Sports & Entertainment.

Request for Free Sample: https://qualiketresearch.com/reports-details/Global-Machine-Vision-Market

Regional Analysis

Global Machine Vision Market is segmented into five regions Americas, Europe, Asia-Pacific, and the Middle East & Africa. A high number of providers with local roots are present in North America, which has the greatest market for machine vision because of the region's early adoption of manufacturing automation. Its supremacy in this market is also a result of the semiconductor sector's dominance in the North American region, a crucial sector for MV systems. Europe is the second-largest market for machine vision, thanks to a robust industrial sector and rising automation demand. Some of the top machine vision manufacturers and suppliers are based in this area.

 Key Players

This report includes a list of numerous Key Players, Allied Vision Technologies GmbH, Basler AG, Cognex Corporation, Keyence Corporation, LMI Technologies, Inc., Microscan Systems, Inc., National Instruments Corporation, OMRON Corporation, Sick AG, Tordivel AS.

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Shortwave Infrared (SWIR) Market Size, Share & Industry Trends Growth Analysis Report by Camera, Lenses, Spectral Imaging, Area & Line Scan, Active & Passive Thermal Imaging, Pushbroom, Snapshot, Security & Surveillance, Monitoring & Inspection, Technology, Vertical and Region – Global Forecast to 2029

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Houston Texas Appliance Parts: Smart sensors need smart power integrity analysis

Houston Texas Appliance Parts

Smart sensors need smart power integrity analysis

by Houston Texas Appliance Parts on Wednesday 22 February 2023 06:49 PM UTC-05

I spy with my little eye…a thousand other eyes looking back at me. Almost everywhere we look in today's world, we find image sensors. The typical smartphone has at least two, and some contain up to five. Devices such as security cameras, in-car cameras, smart doorbells, and baby monitors can look in all directions, keep a watchful eye open 24 hours a day, and alert us when something happens. Drones use cameras to see where they are going and to capture images that are sometimes utilitarian, sometimes breathtaking, and sometimes otherwise impossible for humans to see. And these are just the sensors most of us see and use in our daily life. Sensors also perform manufacturing and quality control on manufacturing lines around the world, scan deep within our bodies to detect disease or injury without surgery, and monitor thousands of planes, trains, ships, and trucks every day.  

Image sensor capacity, performance, cost, and usability have all radically improved in the past decade. Every sensor technology change opens up more possibilities for how and where they can be used. Every new sensor application seems to spark yet another idea of making some aspect of our lives easier, safer, faster, or more enjoyable. For example, image sensors connected to the internet, which includes all those cell phones, doorbell cameras, and drones, is one of the fastest growing segments of this market, with a 34% compound annual growth rate (CAGR) through 2025 (figure 1).  

Figure 1. Sensors linked to the internet are projected to grow significantly by 2025. (Source: O-S-D Report 2021, IC Insights)

One of the most obvious growth trends in sensor technology is in image sensor pixel count. in 2007, the original iPhone launched with a 2MP camera. By 2014, the iPhone 6 camera was at 8MP. Today the iPhone 13 Pro has a 12MP sensor, representing a 12% annual CAGR over the years. Of course, that isn't even close to the highest pixel count on a smartphone. The Samsung Galaxy S22 Ultra has a 108MP sensor, which puts the CAGR for their sensor MP at 33% over the last 14 years. And this growth isn't stopping—there are many projects in the works for 300+MP image sensors.  

Image sensors are also dramatically growing in complexity on multiple levels, driven by four main system objectives— image quality, power, capability, and cost. Rarely are these constraints independent, and each chip design prioritizes different objectives, depending on the intended application space.  

These prioritized system objectives drive different design choices for the image sensor chip and the overall system. Now that 2.5D and 3D stacking of chips is a commodity process, many image sensor companies are taking advantage of layering to drive new image sensor solutions (figure 2). The technology in these stacked solutions typically fall into two broad categories—in-pixel processing to improve the quality and reduce the cost of the image capture itself, and on-die processing to minimize power and cost and improve security [1].  

Figure 2. (Left) Conventional image sensing system, where the pixel array and the read-out circuitry are laid out side by side in the sensor, which transfers data through the energy-hungry and slow MIPI interface to the host for downstream tasks. (Right) Different die-stacked image sensor configurations. Future image sensors will stack multiple layers consisting of memory elements and advanced computation logics. Layers are connected through hybrid bonding (HB) and/or micro through-silicon via (uTSV), which offer orders of magnitude higher bandwidth and lower energy consumption compared to MIPI. (Source: ACM Computer Architecture Today. Used by permission of the author.)

Researchers at Harvard recently developed an in-sensor processor that can be integrated into commercial silicon image sensor chips. On-die image processing, in which important features are extracted from raw data by the image sensor itself instead of a separate microprocessor, speeds up visual processing [2]. Sony just released a sensor that can simultaneously output full-pixel images and high-speed "regions of interest." This combination of sensor and on-die processing allows the solution to simultaneously output an entire scene, like a traffic intersection, as well as high-speed objects of interest, such as license plates or faces, greatly reducing overall system communication bandwidth while increasing response time.  

In another approach to sensor integration, Sony's newest image sensor design [3] separates the photodiodes and pixel transistors that are normally placed on the same substrate, places them on different substrate layers, and then integrates them together with 3D stacking. The result is a sensor that approximately doubles the saturation signal level (essentially its light-gathering capability) to significantly improve the dynamic range and reduce noise. Sony expects this technology will enable increasingly high-quality imaging in smartphone photography without necessarily increasing the size of the smartphone sensor. Of course, there is also a set of solutions that combines both methods, such as augmented reality (AR) image sensors, with their combination of lowest power, best performance, and minimal form factor [4]. 

And then there is the quanta image sensor (QIS)—a new technology developed by Gigajot in which the image sensor contains hundreds of millions to billions of small pixels with photon-number resolving and high dynamic range (HDR) capabilities. The QIS will potentially enable ultra-high pixel resolution that provides a far superior imaging performance compared to charge coupled devices (CCD) and conventional complementary metal oxide semiconductor (CMOS) technologies. While the QIS is not yet in commercial production, test chips have been fabricated using a CMOS process with two-layer wafer stacking and backside illumination, resulting in a reliable device with an average read noise of 0.35 e‑ rms at room temperature operation [5]. 

Not surprisingly, as the complexity of these image sensor chips increases, design, verification, and reliability become more challenging. As image sensor and compute elements come together, the traditional rule of separating analog and digital power domains is, of necessity, violated. Analog and digital components operating within the same pixel must be accurately modeled for functionality, performance, and reliability. This analysis must include both the dynamic loading of the power grid from both sensing and computation functions, and how those functions generate heat and draw current. All of this processing generates more heat and draws additional current from the power grid, both of which can degrade the pixel's ability to capture light.   

According to Dr. Eric R Fossum, Krehbiel Professor for Emerging Technologies at Dartmouth, Queen Elizabeth Prize Laureate, and one of the world's leading experts in CMOS image sensors, 

"Power management by design is very important in image sensors, especially those with high data rates or substantial on-chip computation. Power dissipation leads to heating, which in turn increases the 'dark signal' in the pixel photodiodes—the signal generated even when there is no light. Since each pixel's dark signal may be different, an additive 'fixed-pattern image' of background signal is generated that is difficult to calibrate out of the desired image. The dark signal also contains temporal noise that further affects the low-light imaging capability of the image sensor. The addition of mixed-signal and digital-signal processing and computing in a 3D stacked image sensor further exacerbates the heating problem. Design tools to simulate and manage power dissipation are helpful to eliminate these sources of image quality deterioration during the design process."  

These complexities mean that voltage (IR) drop and electromigration (EM) analysis can't be left until the very end of the design cycle as a "checkbox signoff," because the market risk of performance or chip manufacturing failures is too high. Thorough EM and IR analysis must now be an integral part of the image sensor design flow, which includes the image sensor and its data channel, as well as the high-performance processing connected to the sensor on the same die.  

However, such analysis is complicated by the large amount of analog content in these image sensors. Image sensor designers must be able to analyze and verify both analog and digital power integrity, which means analyzing analog designs in the 10s or 100s of millions of transistors—far beyond the 1-2M transistors that existing tools can handle. While traditional digital EM/IR analysis tools can easily process the digital portions of these die, a complete and scalable EM/IR solution for analog content has been lacking.  

Super-block and chip-level analysis have traditionally been performed manually, using simplifications such as sub-setting of the design, employing less accurate simulators, and other ad hoc methods and approximations, all of which consume large amounts of engineering time to make up for the lack of automated tool support. Neither static analysis nor hand calculations provide the full coverage or confidence of simulation-based signoff. In addition, existing tools tend to create large numbers of false errors for typical analog layouts, requiring even more time and resources for debugging. This lack of detailed automated EM/IR analysis for large-scale analog circuits puts the whole image sensor system at risk. 

In 2021, Siemens EDA introduced the mPower platform, which brings together analog and digital EM, IR drop, and power analysis in a complete, scalable solution for all designs at all nodes [6]. The mPower Analog high-capacity (HC) dynamic analysis provides EM/IR analysis on circuits of hundreds of millions of transistors—just the thing that these large-scale integrated sensors need. mPower HC dynamic analysis provides full-chip and array analyses from block-level SPICE simulations, giving designers the detailed analyses needed to confidently sign off on these large, complex sensor designs for manufacturing while enabling faster overall turnaround times. It can also enable faster iterations earlier in the design cycle by using pre-layout SPICE simulations. At the same time, the mPower Digital solution provides digital power integrity analysis with massive scalability to enable design teams to analyze the largest designs quickly and accurately. Together, the mPower Analog and Digital tools provide an unparalleled ability to model and analyze the IR drop and EM of a complete integrated sensor system, whether it is on one die or many.  

Khandaker Azad, senior manager at ONSEMI in Santa Clara, had this to say after implementing the mPower tool, "We're seeing significant improvement in the quality of EM/IR signoff by doing high-capacity dynamic EM/IR of the digital and analog blocks with the mPower tool. Its scalability, TCL-based flow, and above all, fast runtimes helped us cut down our turnaround time by severalfold. In summary, the mPower tool certainly brought confidence to our full-chip signoff analysis."  

As sensor designs continue to proliferate and evolve in complexity, the need for a scalable, innovative power integrity analysis solution will continue to grow with them. With the mPower platform, there is finally an IC power integrity analysis tool that is up to the task.  

References 

[1] Zhu, Yuhao. "Opportunities and Challenges of Computing in Die-Stacked Image Sensors." Computer Architecture Today, The ACM Special Interest Group on Computer Architecture, Jan. 22, 2022. https://www.sigarch.org/opportunities-and-challenges-of-computing-in-die-stacked-image-sensors/ 

[2] Harvard John A. Paulson School of Engineering and Applied Sciences, Aug. 25, 2022, "Silicon image sensor that computes," [Press release]. https://www.seas.harvard.edu/news/2022/08/silicon-image-sensor-computes  

[3] Sony Semiconductor Solutions Group, Dec. 16, 2021, "Sony Develops World's First Stacked CMOS Image Sensor Technology with 2-Layer Transistor Pixel," [Press release]. https://www.sony-semicon.com/en/news/2021/2021121601.html  

[4] C. Liu, S. Chen, T. -H. Tsai, B. de Salvo and J. Gomez, "Augmented Reality – The Next Frontier of Image Sensors and Compute Systems," 2022 IEEE International Solid- State Circuits Conference (ISSCC), 2022, pp. 426-428, doi:10.1109/ISSCC42614.2022.9731584.  

[5] Ma, J., Zhang, D., Robledo, D. et al., "Ultra-high-resolution quanta image sensor with reliable photon-number-resolving and high dynamic range capabilities," Sci Rep 12, 13869 (2022). https://doi.org/10.1038/s41598-022-17952-z  

[6] Siemens Digital Industries Software, Sept. 28, 2021, "Siemens introduces mPower power integrity solution for analog, digital and mixed-signal IC designs," [Press release]. https://www.plm.automation.siemens.com/global/en/our-story/newsroom/siemens-mpower-power-integrity-analysis/101904 

Author 

Joseph Davis is senior director of product management for Calibre interfaces and mPower power integrity analysis tools at Siemens Digital Industries Software, where he drives innovative new products to market. Prior to joining Siemens, Joseph managed yield simulation products and yield ramp projects at several leading semiconductor fabs, directing yield improvement engagements with customers around the world and implementing novel techniques for lowering the cost of new process technology development. Joseph earned his Ph.D. in Electrical and Computer Engineering from North Carolina State University. He can be reached at davis.joseph@siemens.com. 

The post Smart sensors need smart power integrity analysis appeared first on EE Times.

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