#Line Scan Camera Market Analysis

[rohanshine18]

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

[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!

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[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.

Request A Free Sample:  https://qualiketresearch.com/request-sample/Global-Machine-Vision-Market/request-sample  

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.

About Us:

QualiKet Research is a leading Market Research and Competitive Intelligence partner helping leaders across the world to develop robust strategy and stay ahead for evolution by providing actionable insights about ever changing market scenario, competition and customers.

QualiKet Research is dedicated to enhancing the ability of faster decision making by providing timely and scalable intelligence.

QualiKet Research strive hard to simplify strategic decisions enabling you to make right choice. We use different intelligence tools to come up with evidence that showcases the threats and opportunities which helps our clients outperform their competition. Our experts provide deep insights which is not available publicly that enables you to take bold steps.

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[shalu34]

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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[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

[usacounselingcredit]

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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Scan Camera Market Disclosing Latest Developments and Technology Advancements – 2028 |Line Scan Camera Market are Teledyne Technologies, Basler AG (Basler)

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North America, US, Canada, Latin America, Brazil, Mexico, Rest of Latin America, Western Europe, Germany, UK, France, Spain, Italy, Benelux, Nordic, Rest of Western Europe, Eastern Europe, Russia, Poland, Rest of Eastern Europe, Asia Pacific, China, Japan, India, South Korea, Australia, ASEAN (Indonesia, Vietnam, Malaysia, etc.), Rest of Asia Pacific, Middle East & Africa, GCC, South Africa, Turkey and Rest of the Middle East & Africa.

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𝐓𝐚𝐛𝐥𝐞 𝐎𝐟 𝐂𝐨𝐧𝐭𝐞𝐧𝐭: 1. Scan CameraMarket Introduction 1.1. Definition 1.2. Research Scope 2. Executive Summary 2.1. Key Findings by Major Segments 2.2. Top strategies by Major Players 3. Global Scan CameraMarket Overview 3.1. Scan CameraMarket Dynamics 3.1.1. Drivers 3.1.2. Opportunities 3.1.3. Restraints 3.1.4. Challenges 3.2. COVID-19 Impact Analysis in Global Scan CameraMarket 3.3. PESTLE Analysis 3.4. Opportunity Map Analysis 3.5. PORTER’S Five Forces Analysis 3.6. Market Competition Scenario Analysis 3.7. Product Life Cycle Analysis 3.8. Manufacturer Intensity Map 3.9. Major Companies sales by Value & Volume 𝐂𝐨𝐧𝐭𝐢𝐧𝐮𝐞.

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Machine Vision Camera Market - Forecast 2022 - 2027

Machine Vision Camera Market Overview

Machine Vision Camera Market Size is forecast to reach $2.2 billion by 2027, at a CAGR of 9.9% during the forecast period 2022-2027. The need for inspection of flaws and controlling a specific task of industrial operations is motivating the utilization of Machine Vision Cameras in process control and quality control applications. Additionally, the growing penetration of automation, machine monitoring, real time process control solutions and robotics across various industries and rapid advancements in industrial technologies along with the need for higher productivity are boosting the deployment of Machine Vision Cameras. Machine vision cameras depend on digital sensors with specialized optics to capture images, in order to process, analyze, and measure various characteristics by using computer hardware and software for accurate decision making. The rising online shopping and e-commerce sales have contributed to the growth of the market in this region. According to Australian Bureau of Statistics data, online sales in Australia recorded a 55% rise in December 2020, when compared to last year data. Also, According to Australia Post’s Online Shopping Report published in January 2021, that over 5.6 million households shopped online in December 2020, a 21.3% higher when compared to 2019 data. A machine vision camera can easily inspect minute object details which are too small to be seen by human eye if it is built around the right resolution and optics. These systems based on both Complementary Metal-Oxide Semiconductor sensor and Charged Coupled device sensor encounter a wide range of applications in various industry verticals including oil& gas, aerospace, transportation, automotive among others and are able to serve their inspection needs with the available types such as PC-based and smart camera-based Machine Vision Cameras.

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Report Coverage The report: “Machine Vision Camera Market Report– Forecast (2022-2027)”, by IndustryARC covers an in-depth analysis of the following segments of the Brushless DC Motor market Report.

By Sensor Type: CCD, CMOS, MIG Sensor, N-Type MOS Sensor By Platform: Smart Camera, PC Based Camera, Wireless Camera, Wearable Camera By Camera Type: Line Scan Camera, Area Scan Camera By Process Type: 1D, 2D, 3D By Pixel Type: Less than 1MP, 1-3MP, 3-5MP, 5-8MP, 8-12MP, More than 12MP By Spectrum Type: Infrared, X-Ray, Visible Light and Others By Lens Type: Wide Angle Lens, Normal Lens, Telephoto Lens By Application: Quality Assurance and Inspection, Position Guidance, Measurement, Identification, Pattern Recognition and Others By End Users: Automotive, Electrical and Electronics, Healthcare, Consumer Electronics, Aerospace and Defense, Logistics, Security and Surveillance, Printing, ITS, Machinery, Packaging, Food and Beverage and Others By Geography: North America (U.S, Canada, Mexico), South America (Brazil, Argentina, Chile, Colombia and Others), Europe (Germany, UK, France, Italy, Spain, Russia and Others), APAC (China, Japan India, Australia and Others), and RoW (Middle East and Africa)

Key Takeaways

The rising need for advanced manufacturing in the U.S have increasingly demanded the use of Machine Vision Cameras. The market players are majorly opting for various strategies such as product launch, partnership and agreements and collaborations to gain market traction and further penetration to explore the hidden opportunities in upcoming trends including Industry4.0 Recognizing trends and irregularities in production processes early on machine vision paves the way for realizing the smart factory of the future. Machine vision ensures safety in production process as well as quality in the end product.

Machine Vision Camera Market Segment Analysis - By Lens Type

Based on the Lens type the Machine Vision Camera Market is segmented into wide-angle lens, normal lens, and telephoto lens. Wide Area Lens dominates the market with share of 39.4% in 2021. Growth in various machine vision applications such as mobile mapping, UAV-based inspections of power lines or facilities, and advanced automotive ADAS systems has driven demand for a wide-angle lens that provides a large field of view and high resolution. Telephoto lenses are dominating the market among the other lenses. This is owing to the increasing demand automotive and electronics industries for inspection of three-dimensional parts using machine vision.

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Machine Vision Camera Market Segment Analysis - By End Use Industry Automotive industry is expected to witness a highest CAGR of 14.1% the forecast period 2022-2027, owing to increasing investments, and funds for semiconductors offering opportunities for the adoption of automation technology which is further set to drive the demand of connectors in semiconductor industry. These systems encounter wide range of applications in various industry verticals including oil& gas, aerospace, transportation, automotive among others and are able to serve their inspection needs with the available types such as PC-based and smart camera based Machine Vision Cameras. Investments by the U.S automakers for strengthening of the manufacturing of automobiles with increasing integration of recent robotic vision technologies in vehicles is accompanying the growth of the robotic vision market in the U.S. Industry revenue is projected to continue grow due to this development.

Machine Vision Camera Market Segment Analysis - By Geography Machine Vision Camera market in Europe region held significant market share of 38% in 2021. The investments are rising for electric, connected and autonomous vehicles and this in turn The U.S. accounted a huge market base for Machine Vision due to the growing adoption of Machine Vision Camera technology by vision companies, and the companies continue to witness exploration for new applications in a variety of industries, thus driving the machine vision market driven by a push from companies such as Google and Verizon. The rising initiatives in Middle East and Africa for the increasing need of automation is set to propel the machine vision market. The growth of manufacturing industry in Africa and Middle East (AME) is expected to grow at a rate of 14.2% between 2021 and 2025 thereby significantly driving the market

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Machine Vision Camera Market Drivers

Growing Demand for Smart Cameras Smart cameras often support a Machine Vision Camera by digitizing and transferring frames for computer analysis. A smart camera has a single embedded image sensor. They are usually tailored-built for specialized applications where space constraints require a compact footprint. Smart cameras are employed for a number of automated functions, whether complementing a multipart Machine Vision Camera, or as standalone image-processing units. Smart cameras are considered to be an effective option for streamlining automation methods or integrating vision systems into manufacturing operations as they are cost-efficient and relatively easy to use. There is a huge demand for smart cameras in industrial production as manufacturers often use them for inspection and quality assurance purposes. Smart cameras are growing at a 9.7% CAGR with Machine vision being a premier use case. Thus, increasing demand for smart cameras will drive the Machine Vision Cameras market growth in various industrial applications.

Increasing need for quality products, high manufacturing capacity Machine Vision Cameras perform quality tests, guide machines, control processes, identify components, read codes and deliver valuable data for optimizing production. Modern production line are advanced and automated. Machine vision enables manufacturing companies to remain competitive and prevent an exodus of key technologies. Recognizing trends and irregularities in production processes early on machine vision paves the way for realizing the smart factory of the future. Machine vision ensures safety in production process as well as quality in the end product. As a result of this, according to an IDG survey by Insight, 96% of Companies surveyed think computer vision has the capability to boost revenue, with 97% saying this technology will save their organization time and money across the board.

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Machine Vision Camera Market Challenges

Lack of awareness among users and inadequate expertise The robotic vision technology is rapidly changing, with new technologies emerging constantly, and new tools coming to market incredibly fast to make tackling automation problems easier. In the past decade alone, the robotic vision market has seen the introduction of more advanced sensors in terms of both smaller pixels and larger sensors, software platforms that continues to be more accurate, and lighting which is growing brighter and becoming more efficient. The high cost of the research and development in robotic vision and the lack of awareness among users about the rapidly advancing robotic vision technology are key factors likely to hinder the market to an extent.

Machine Vision Camera Industry Outlook Product launches, acquisitions, Partnerships and R&D activities are key strategies adopted by players in the market. Machine Vision Camera top companies includeCognex Omron Corp Sony Corp. Panasonic Corp. Hitachi Basler AG Keyence Corp. National Instruments Sick AG Teledyne Technologies FLIR

Recent Developments

In April 2021, Sick AG launched its first machine vision camera P621 2D equipped with AI technology for complex imaging tasks in surface mount assemblies. In March 2020, Sony launched GS CMOS cameras equipped with CMOS sensors used in integrated transport systems and biometrics. In June 2020, The company launched FH Series Vision system equipped with AI technology for the growing demand for labor-saving automated visual inspection during COVID-19 pandemic.