Light Detection and Ranging Technology for Energy Sector
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The advancement of technologies in the last decade is no surprise to anyone. LiDAR although new to some has been around for some time, haven’t heard of it? What are you missing? This presentation will briefly explain what LiDAR is and how it is gathered, but the primary focus will be on how the Energy Sector can benefit from its use. We will show several example project datasets, tools developed and available to industry, and how this growing technology can change how you look at your projects. Additionally, we will show how involving all members of your exploration team in a LiDAR planning process will save time and money while increasing safety and reducing environmental impact. Your well location may begin with Geology calculating back to a surface location, but the finial location can affect many other decisions your company may make…good planning makes for good decisions. Chris Martin is a Survey Technologist with 17 years of experience in the surveying profession. His field survey career has involved major projects with TransCanada Pipelines and Alliance Pipelines along with many conventional oil and gas programs. For the last 4 years he has primarily focussed on business development and is currently a Marketing Representative for Can-Am Geomatics.
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In a global biometric hardware market worth over $4B, traditional fingerprint/palm sensors still monopolize 95% of the market, but face and iris sensors lie in wait.
Fingerprint technology impressively dominates the market - but changes are expected
Due to historical reasons, like the criminal fingerprint database established by the FBI with ink-based techniques, fingerprint sensing is the most common biometric technology currently used, by far. We estimate that the annual revenues generated by fingerprint-based solutions are currently $4.25B, representing 95% of the hardware market. Fingerprint sensing dominates technologies like iris, face, palm or voice recognition, because it meets almost all the requirements of a “perfect” biometric recognition technology. It is robust, stable and repeatable, time-invariant, difficult to spoof, has a distinctive meaning, is "unique" amongst a population, accessible, easy to use and acceptably non-intrusive. All other biometric technologies do not yet fulfill those requirements as specifically as fingerprint technology. Hardware revenues generated by the other biometric technologies are relatively low, estimated at $250M, mostly from iris and face recognition. This report identifies the players in the biometric hardware market, and provides technology, market and trend evolution insights. The fingerprint market has experienced an incredible volume increase in the consumer market with the adoption of the active capacitance detection on an increasing number of smartphones to answer the demand of online identification, mobile payment and unlocking applications. Diverse technologies, such as optical, thermal, and Piezoelectric Micromachined Ultrasonic Transducers (PMUT), are also trying to penetrate the consumer market, but are still very limited. The industrial and homeland/security markets are still widely using optical technology. Other biometric solutions like iris, face or voice recognition have been introduced but with a limited impact. Their performance hasn’t yet reached the requirements with regards to cost, reliability, false rejection rate and false acceptance rate to significantly penetrate either the consumer, industrial or homeland/security markets....
More information on that report: http://www.i-micronews.com/report/product/sensors-for-biometry-and-recognition.html
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This document describes a multi-camera time-of-flight imaging system that uses multiple synchronized cameras and light sources. It allows control over modulation signals to capture dynamic scenes and extract depth, velocity and non-line-of-sight motion information. The system architecture uses a direct digital synthesis chip to generate programmable modulation signals, synchronized signal conditioning circuits and a real-time controller to coordinate image capture across multiple time-of-flight cameras. It aims to enable applications like phased array imaging, simultaneous Doppler velocity capture and detecting motion behind scattering media.
Imaging Technologies for Automotive 2016 Report by Yole Developpement
Imaging technology, which is currently mainly cameras, is exploding into the automotive space, and is set to grow at 20% CAGR to reach $7.3B in 2021
INFOTAINMENT AND ADVANCED DRIVER ASSISTANCE SYSTEMS (ADAS) PROPEL AUTOMOTIVE IMAGING
Since 2008, when a recession acted as a wakeup call to the whole industry, the automotive market has undergone obvious structural change. Capitalizing on technologies initially developed for smartphones, electronics have invaded, and imaging technology is now taking center stage. From less than one camera per car on average in 2015, there will be more than three cameras per car by 2021, which means 371 million automotive imaging devices.
Cameras were initially mounted for ADAS purposes on high-end vehicles, with deep learning image analysis techniques promoting early adoption. The Israeli company Mobileye has been instrumental in bringing this technology to market, along with On Semiconductor, which provided the CMOS image sensor. Copycat competition will probably pick up as the market now justifies initial investment in design and technology. It is now a well-established fact that vision-based autonomous emergency braking (AEB) is possible and saves life. Adoption of forward ADAS cameras will therefore accelerate.
Growth of imaging for automotive is also being fueled by the park assist application, and 360° surround view camera volume is skyrocketing. While it’s becoming mandatory in the US to have a rearview camera, that uptake is dwarfed by 360° surround view cameras, which enable a “bird’s eye view” perspective. This trend is most beneficial to companies like Omnivision at sensor level and Panasonic and Valeo, which have become the main manufacturers of automotive cameras.
Mirror replacement cameras are currently the big unknown and take-off will primarily depend on its appeal and car design regulation. Europe and Japan are at the forefront of this trend, which should become slightly significant by 2021.
Solid state lidar is well talked about and will start to be found in high end cars by 2021. Cost reduction will be a key driver as the push for semi-autonomous driving will be felt more strongly by car manufacturers. The report will analyse the impact of lidar for automotive vision in detail.
Night vision cameras using Long Wave Infrared (LWIR) technology were initially perceived as a status symbol. However, they’re increasingly appreciated for their ability to automatically detect pedestrians and wildlife. LWIR will therefore become integrated into ADAS systems in future.
3D cameras will be limited to in-cabin infotainment and driver monitoring. This technology will be key for luxury cars and therefore is of limited use today.
If any significant semi-autonomous trend picks up, the technology will probably become mandatory, due to safety issues.
More information on that report at http://www.i-micronews.com/reports.html
Sensors and Data Management for Autonomous Vehicles report 2015 by Yole Devel...
Multiple sensing technologies will ensure many market opportunities for Tier 1 players, Tier 2 players, and newcomers alike
Sensor technologies are a driving force in making fully autonomous vehicles a reality. Automakers are racing to develop safe self-driving cars, but this race is a distance run more than a sprint, where multiple automation stages will imply multiple sensors. Ultrasonic sensors, radars, and multiple cameras systems are already embedded in high-end vehicles -- and within 10 years, they could also include long-range cameras, LIDAR, micro bolometer and accurate dead reckoning. These devices will work concurrently and each technology will support another to ensure codependency and avoid concerns. Even though sensors are only part of the puzzle, their market opportunities are promising.
Status of the CMOS Image Sensor Industry 2016: New Dynamics in Market and Tec...
New functions are pushing change in CMOS image sensors, boosting the market toward $18.8B in 2021 at 10.4% CAGR
Beyond $10B: The CMOS image sensor industry keeps growing at high pace
Driven by renewed mobile and automotive applications, the CMOS image sensor (CIS) industry is expected to expand at a compound annual growth rate (CAGR) of 10.4% from 2015 to 2021, reaching US$18.8B market value by 2021.
Yole Développement expects sustained growth of the CMOS image sensor industry for the next five years. Increasing camera content in smartphones will more than offset slower smartphone volume growth. The trend for dual and 3D cameras will have a major impact on CIS volumes. While it is too early to fully describe the strategy of the main actors yet, some products are already on the market. The 2016 report comprehensively covers key market and technology choices.
One big story this year is the consumer market, which is recovering from the total collapse of digital photography. While action cameras seem to have reached a ceiling, new applications such as drones, robots, virtual reality and augmented reality are ready to rejuvenate this emblematic market. The Automotive camera market has established itself as a key growth market for CIS. The Advanced Driver Assistance (ADAS) trend is further increasing pressure on vendors to provide sensors beyond their current technical capabilities. Image analysis is the new frontier and early usage of artificial intelligence is catching people’s imagination. We are therefore in the middle of an explosive growth pattern that will not slow down before 2021. An exceptionally high 23% CAGR is predicted in automotive for the 2015-2021 period.
Introduction to lidar and its application
LiDAR acronym as Light Detection and Ranging is remote sensing technology having several technical and socialite advantages. This technology is basically used to make high resolution digital map to provide the real time data. This data can be processed and used to extract the useful information. A typical LIDAR system consists of three main components, a GPS system to provide position information, an INS unit for attitude determination, and a LASER system to provide range (distance) information between the LASER firing point and the ground point. In addition to range data, modern LIDAR systems can capture intensity images over the mapped area. Therefore, LIDAR is being more extensively used in mapping and GIS applications.
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Sensors for Biometry and Recognition - 2016 Report by Yole Developpement
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Laser Scanning
Laser scanning is an emerging data acquisition technology that has remarkably broadened its
application field and has been a serious competitor to other surveying techniques. Due to rapid
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1 Airborne laser scanning
Airborne laser scanning is an active remote sensing technology that is able to rapidly collect data
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Airborne laser scanning is often coupled with airborne imagery, therefore the point clouds and
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Figure 1.: Time of flight laser range measurement [2]
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The components are shown in Figure 2
Figure 2.: Principle of airborne LiDAR [2]
2. Sensors, equipment
Sensors can be distinguished based on the scanning method, i.e. how the laser beam is directed
through the surface. The four most widely used sensor types are shown in Figure 4.2.3.
Figure .3: Scanning mechanisms [1]
As it is clearly seen in Figure 3, different kinds of mechanisms are applied by the different types
of sensors; each has its advantages and shortcomings, e.g. number of moving parts, type of
rotation etc. that lead to different kinds of error sources.
The capabilities (repetition rate, scan frequency, scan angle, point density) of the above scanners
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depends on the flying speed. To avoid too wide spacing of points along flight direction, LiDAR
flights are usually slower (e.g. at 60-80 m/sec) compared to that of photogrammetric flights
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density, however, due to technical and microelectronic reasons (regarding the operating
mechanism of the mirror, especially in case of oscillating mirrors), higher point density can be
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bc_fp_lidar_pres_moskal.ppt
LiDAR uses laser pulses to measure distances to surfaces and create 3D point clouds. It provides accurate elevation data day or night. Sources of error include scan angle, strip adjustment, ground point selection, interpolation, and visualization. Acquisition factors like flight parameters and weather affect data quality. Processing involves correcting systematic errors between flight lines and filtering points. Derivative products include DEMs, DTMs, and intensity images. Validation is needed due to inherent errors introduced during data collection and processing.
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In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
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- 4. What is LiDAR? Terrestrial Scanners Airborne Systems Two most common types of systems:
- 5. LiDAR sensors are attached to an aircraft and send pulses of light toward the ground. The time difference between the sending of the pulse and the return of the reflected light to the aircraft is then measured. Airborne Systems
- 6. The aircraft uses GPS and inertial measurement units to reference its location to a ground based control system. This produces an accurate x,y,z position. Airborne Systems
- 7. Dataset is processed by the LiDAR vendor to remove noise and to separate ground points from surface features, such as trees, buildings, power lines etc... Bare Earth (Ground Points) Full Feature LiDAR Processing
- 9. LiDAR data in remote area of NW Alberta LiDAR Datasets Energy Sector
- 15. 3D models help facilitate the efficient use of the LiDAR Hill Shade Contours Slope Map Color Ramp Terrain Models
- 17. Contour Maps
- 21. Using internet based meeting tools we will host virtual Operations Meetings, including the clients, field offices, surveyors and consultants all at the same time. Can-Am Office Consultant Field Office Real Time Operations Meetings
- 22. Sample Meeting Can-Am Office Consultant Field Office Real Time Operations Meetings
- 27. Operation Meetings Profile With Cross slope Profile Details
- 31. Thanks for your time! Schedule a Demo