2015 D-STOP Symposium session by Ram Mirwani of AWR/National Instruments.
Get symposium details: http://ctr.utexas.edu/research/d-stop/education/annual-symposium/
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/nxp/embedded-vision-training/videos/pages/may-2018-embedded-vision-summit-roy
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Arunesh Roy, Radar Algorithms Architect at NXP Semiconductors, presents the "Understanding Automotive Radar: Present and Future," tutorial at the May 2018 Embedded Vision Summit.
Thanks to its proven, all-weather range detection capability, radar is increasingly used for driver assistance functions such as automatic emergency braking and adaptive cruise control. Radar is considered a crucial sensing technology for autonomous vehicles not only for its range finding ability, but also because it can be used to determine target velocity and target angle. In this tutorial, Roy introduces the basic principles of operation of a radar system, highlighting its main parameters and comparing radar with computer vision and other types of sensors typically found in ADAS and autonomous vehicles.
After examining the features and the limitations of current automotive radar systems, Roy discusses how automotive radar is evolving, particularly in light of safety performance assessment programs such as the European New Car Assessment Programme (eNCAP). He concludes with a discussion of how radar systems may compete with or complement vision-based sensors in future ADAS-equipped and autonomous vehicles.
Radar Technologies For Automotive 2018 report by Yole Développement Yole Developpement
How will radar sensor technology shape the cars of the future? Prepare for the automotive sensor industry’s golden age, in which radar will be increasingly viewed as a key technology for autonomous vehicles.
AUTOMOTIVE IS EXPERIENCING AN EXPLOSION OF NEW HIGH-TECH APPLICATIONS
Automatic emergency braking, adaptive cruise control, and lane-change assist are some examples of these new applications. Spurred by the New Car Assessment Program, OEMs are designing cars with numerous sensors that enable applications like these. And since most of these new applications are safety-related, the sensors must be highly accurate. This means very tight specifications for object detection and classification, as well as being ultra-reliable: operable in every weather condition, in poor lighting, near or far, and with a wide field of view. Radar technology is well-suited to fulfill most of these requirements. We say “most” because object classification is not currently possible with radar, but certain companies are moving quickly to unlock this capability in imaging radar.
Radar has an impressive technology roadmap allowing for huge resolution improvement as well as device miniaturization and cost reduction. Despite small growth (~3%) in global car sales until 2022, Yole Développement expects an average growth rate of 25% for radar module sales, and an average growth rate of 22% for radar chip sales over the next five years - with autonomous driving being the next long-term driver for radar technology growth.
More information on that report at http://www.i-micronews.com/reports.html
Describes basics of automotive radar, working principle and future development in automotive radar sector. Role of radar sensor in development of future ACC, ADAS system.
Continental, Veoneer, ZF, Valeo, Bosch, Aptiv, Denso, Ainstein: Discover the technologies used in the main Radar Systems and Chipsets.
More information on that report at: https://www.systemplus.fr/reverse-costing-reports/automotive-radar-comparison-2018/
For the full video of this presentation, please visit:
http://www.embedded-vision.com/platinum-members/nxp/embedded-vision-training/videos/pages/may-2016-embedded-vision-summit
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Tom Wilson, ADAS Product Line Manager at NXP Semiconductors, presents the "Sensing Technologies for the Autonomous Vehicle" tutorial at the May 2016 Embedded Vision Summit.
Autonomous vehicles will necessarily utilize a range of sensing technologies to see and react to their surroundings. We are witnessing dramatic advances not just for embedded vision, but also in complementary technologies like radar and LiDAR. Each of these sensing technologies provides unique capabilities for giving a vehicle a complete view of its surroundings. This presentation compares vision-based sensing with complementary sensing technologies, explores key trends in sensors for autonomous vehicles, and analyses challenges and opportunities in fusing the output of multiple sensor technologies to enable robust perception and mapping for autonomous vehicles.
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/nxp/embedded-vision-training/videos/pages/may-2018-embedded-vision-summit-roy
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Arunesh Roy, Radar Algorithms Architect at NXP Semiconductors, presents the "Understanding Automotive Radar: Present and Future," tutorial at the May 2018 Embedded Vision Summit.
Thanks to its proven, all-weather range detection capability, radar is increasingly used for driver assistance functions such as automatic emergency braking and adaptive cruise control. Radar is considered a crucial sensing technology for autonomous vehicles not only for its range finding ability, but also because it can be used to determine target velocity and target angle. In this tutorial, Roy introduces the basic principles of operation of a radar system, highlighting its main parameters and comparing radar with computer vision and other types of sensors typically found in ADAS and autonomous vehicles.
After examining the features and the limitations of current automotive radar systems, Roy discusses how automotive radar is evolving, particularly in light of safety performance assessment programs such as the European New Car Assessment Programme (eNCAP). He concludes with a discussion of how radar systems may compete with or complement vision-based sensors in future ADAS-equipped and autonomous vehicles.
Radar Technologies For Automotive 2018 report by Yole Développement Yole Developpement
How will radar sensor technology shape the cars of the future? Prepare for the automotive sensor industry’s golden age, in which radar will be increasingly viewed as a key technology for autonomous vehicles.
AUTOMOTIVE IS EXPERIENCING AN EXPLOSION OF NEW HIGH-TECH APPLICATIONS
Automatic emergency braking, adaptive cruise control, and lane-change assist are some examples of these new applications. Spurred by the New Car Assessment Program, OEMs are designing cars with numerous sensors that enable applications like these. And since most of these new applications are safety-related, the sensors must be highly accurate. This means very tight specifications for object detection and classification, as well as being ultra-reliable: operable in every weather condition, in poor lighting, near or far, and with a wide field of view. Radar technology is well-suited to fulfill most of these requirements. We say “most” because object classification is not currently possible with radar, but certain companies are moving quickly to unlock this capability in imaging radar.
Radar has an impressive technology roadmap allowing for huge resolution improvement as well as device miniaturization and cost reduction. Despite small growth (~3%) in global car sales until 2022, Yole Développement expects an average growth rate of 25% for radar module sales, and an average growth rate of 22% for radar chip sales over the next five years - with autonomous driving being the next long-term driver for radar technology growth.
More information on that report at http://www.i-micronews.com/reports.html
Describes basics of automotive radar, working principle and future development in automotive radar sector. Role of radar sensor in development of future ACC, ADAS system.
Continental, Veoneer, ZF, Valeo, Bosch, Aptiv, Denso, Ainstein: Discover the technologies used in the main Radar Systems and Chipsets.
More information on that report at: https://www.systemplus.fr/reverse-costing-reports/automotive-radar-comparison-2018/
For the full video of this presentation, please visit:
http://www.embedded-vision.com/platinum-members/nxp/embedded-vision-training/videos/pages/may-2016-embedded-vision-summit
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Tom Wilson, ADAS Product Line Manager at NXP Semiconductors, presents the "Sensing Technologies for the Autonomous Vehicle" tutorial at the May 2016 Embedded Vision Summit.
Autonomous vehicles will necessarily utilize a range of sensing technologies to see and react to their surroundings. We are witnessing dramatic advances not just for embedded vision, but also in complementary technologies like radar and LiDAR. Each of these sensing technologies provides unique capabilities for giving a vehicle a complete view of its surroundings. This presentation compares vision-based sensing with complementary sensing technologies, explores key trends in sensors for autonomous vehicles, and analyses challenges and opportunities in fusing the output of multiple sensor technologies to enable robust perception and mapping for autonomous vehicles.
multi mission radar (MMR) - EL/M-2084 FOR IRON DOMEHossam Zein
multi mission radar (MMR) - EL/M-2084 FOR IRON DOME
from IAI MELTA
for more detailed info. visit -::-
http://hossamozein.blogspot.com/2011/10/iron-dome.html
This is a presentation that focuses on autonomous vehicles technology. The presentation describes key sensor technologies integrated under the bonnet of a driverless car. After a brief introduction, the presentation dwells deeper into each sensor technology demonstrating examples of self driving cars such as Google's self driving car, DARPA URBAN challenge etc., along the way. It also introduces the concept of electronic control units which is responsible for collecting data from different sensors and respond to other units accordingly. The slides also build a platform for vehicle to vehicle communication technology, types and its application areas.
CAS is a system designed to help prevent rear‐end collisions with vehicles which are stationary or travelling in the same direction.
It uses visual and audio warnings to prompt the driver to take preventative action.
It also initiates braking if the driver fails to respond to the warnings.
There are generally two kinds of safety systems in automobiles:-
Passive safety – seatbelts, airbag system
Active safety – impact sensors, radar detection
CAS is a system designed to help prevent rear‐end collisions with vehicles which are stationary or travelling in the same direction. Several studies have shown that driver distraction or inattentiveness is a factor in the great majority of rear end accidents. The system is aimed at alerting the driver to an imminent rear end collision both at low speeds, typical of urban driving, and at higher speeds typical of rural roads and highways.
Abstract—Collision warning and collision avoidance systems are emerging automotive safety technologies that assist drivers in avoiding rear-end collisions. Their function is to allow the driver enough time to avoid the crash and yet avoid annoying the driver with alerts perceived as occurring too early or unnecessary. The purpose of this paper is to review various mechanisms under development or developed rear end collision avoidance of automobiles. Some of the reviewed work include an automatic braking system that safely stops an automobile while approaching an obstruction to avoid collision. Another separate but related system is to have a detection device, which alerts the driver in case the automobile veers off the road by crossing either the centre or side painted lines. The braking system senses an obstacle, calculates the relative distance and applies the variable brakes automatically to maintain a safe distance. Warning devices and sensor mechanisms used in obstacle avoidance systems are also reviewed. With the expansion in road network, motorization and urbanization in the country, the number of road accidents have surged. Road traffic injuries (RTIs) and fatalities have emerged as a major public health concern, with RTIs having become one of the leading causes of deaths, disabilities and hospitalizations which impose severe socio-economic costs across the world. Motor vehicle population has grown at a compound annual growth rate (CAGR) of 10 per cent 2000-2009, during fuelled by a rising tide of motorization. Concomitantly, traffic risk and exposure have grown. During the year 2010, there were around 5 lakh road accidents, which resulted in deaths of 134,513 people and injured more than 5 lakh persons in India. These numbers translate into 1 road accident every minute, and 1 road accident death every four minutes. The total number of accidents can be reduced through the safety systems installed in vehicles. However, it was found that many traditional safety measures are reducing their effectiveness.
Just what is that thing on top of the Google Car? What does adaptive cruise control with lane assist mean? When are these things going to be ready? The answer to these questions and more in a technology overview that unravels just how these vehicles are going to work. Presented at the 2017 D-STOP Symposium.
2015 D-STOP Symposium session by Robert Heath, UT Austin's Wireless Networking & Communications Group.
Get symposium details: http://ctr.utexas.edu/research/d-stop/education/annual-symposium/
A compact, cost-effective and high-performance driving assistance system.
The Mid Range Radar Sensor, with its three Transmitter and four Receiver channels, operates in the 76-77 GHz frequency band that is standard for automotive radar applications. The front version works with an aperture angle of up to +/- 45 degrees and can detect objects up to 160 meters away. With a compact design (using fan-out RF components from Infineon), the system is easy to integrate into a vehicle’s body.
The system integrates two electronic boards including Bosch, Freescale and STMicroelectronics circuits. The RF board is manufactured with an asymmetric structure using Hybrid PTFE/FR4 substrate and is equipped with planar antennas.
Infineon 77GHZ SiGe Monolithic Microwave Integrated Circuits (MMIC) are used as High-Freqency transmitter and receiver.. The two RF dies are packaged is the last version of the eWLB, the Fan-Out Wafer level Package developed and manufactured by Infineon.
multi mission radar (MMR) - EL/M-2084 FOR IRON DOMEHossam Zein
multi mission radar (MMR) - EL/M-2084 FOR IRON DOME
from IAI MELTA
for more detailed info. visit -::-
http://hossamozein.blogspot.com/2011/10/iron-dome.html
This is a presentation that focuses on autonomous vehicles technology. The presentation describes key sensor technologies integrated under the bonnet of a driverless car. After a brief introduction, the presentation dwells deeper into each sensor technology demonstrating examples of self driving cars such as Google's self driving car, DARPA URBAN challenge etc., along the way. It also introduces the concept of electronic control units which is responsible for collecting data from different sensors and respond to other units accordingly. The slides also build a platform for vehicle to vehicle communication technology, types and its application areas.
CAS is a system designed to help prevent rear‐end collisions with vehicles which are stationary or travelling in the same direction.
It uses visual and audio warnings to prompt the driver to take preventative action.
It also initiates braking if the driver fails to respond to the warnings.
There are generally two kinds of safety systems in automobiles:-
Passive safety – seatbelts, airbag system
Active safety – impact sensors, radar detection
CAS is a system designed to help prevent rear‐end collisions with vehicles which are stationary or travelling in the same direction. Several studies have shown that driver distraction or inattentiveness is a factor in the great majority of rear end accidents. The system is aimed at alerting the driver to an imminent rear end collision both at low speeds, typical of urban driving, and at higher speeds typical of rural roads and highways.
Abstract—Collision warning and collision avoidance systems are emerging automotive safety technologies that assist drivers in avoiding rear-end collisions. Their function is to allow the driver enough time to avoid the crash and yet avoid annoying the driver with alerts perceived as occurring too early or unnecessary. The purpose of this paper is to review various mechanisms under development or developed rear end collision avoidance of automobiles. Some of the reviewed work include an automatic braking system that safely stops an automobile while approaching an obstruction to avoid collision. Another separate but related system is to have a detection device, which alerts the driver in case the automobile veers off the road by crossing either the centre or side painted lines. The braking system senses an obstacle, calculates the relative distance and applies the variable brakes automatically to maintain a safe distance. Warning devices and sensor mechanisms used in obstacle avoidance systems are also reviewed. With the expansion in road network, motorization and urbanization in the country, the number of road accidents have surged. Road traffic injuries (RTIs) and fatalities have emerged as a major public health concern, with RTIs having become one of the leading causes of deaths, disabilities and hospitalizations which impose severe socio-economic costs across the world. Motor vehicle population has grown at a compound annual growth rate (CAGR) of 10 per cent 2000-2009, during fuelled by a rising tide of motorization. Concomitantly, traffic risk and exposure have grown. During the year 2010, there were around 5 lakh road accidents, which resulted in deaths of 134,513 people and injured more than 5 lakh persons in India. These numbers translate into 1 road accident every minute, and 1 road accident death every four minutes. The total number of accidents can be reduced through the safety systems installed in vehicles. However, it was found that many traditional safety measures are reducing their effectiveness.
Just what is that thing on top of the Google Car? What does adaptive cruise control with lane assist mean? When are these things going to be ready? The answer to these questions and more in a technology overview that unravels just how these vehicles are going to work. Presented at the 2017 D-STOP Symposium.
2015 D-STOP Symposium session by Robert Heath, UT Austin's Wireless Networking & Communications Group.
Get symposium details: http://ctr.utexas.edu/research/d-stop/education/annual-symposium/
A compact, cost-effective and high-performance driving assistance system.
The Mid Range Radar Sensor, with its three Transmitter and four Receiver channels, operates in the 76-77 GHz frequency band that is standard for automotive radar applications. The front version works with an aperture angle of up to +/- 45 degrees and can detect objects up to 160 meters away. With a compact design (using fan-out RF components from Infineon), the system is easy to integrate into a vehicle’s body.
The system integrates two electronic boards including Bosch, Freescale and STMicroelectronics circuits. The RF board is manufactured with an asymmetric structure using Hybrid PTFE/FR4 substrate and is equipped with planar antennas.
Infineon 77GHZ SiGe Monolithic Microwave Integrated Circuits (MMIC) are used as High-Freqency transmitter and receiver.. The two RF dies are packaged is the last version of the eWLB, the Fan-Out Wafer level Package developed and manufactured by Infineon.
The Global Automotive RADAR applications Market Research Report 2016 give insights upon the world's major regional market conditions of the Automotive RADAR applications industry which mainly focus upon the main regions which include continents like North America, Europe and Asia and the main countries i.e. United States, Germany, Japan and China.
Request to Sample of This Report @ https://marketreportscenter.com/request-sample/345256
Delphi Integrated Radar and Camera System (RACam) 2016 teardown reverse costi...Yole Developpement
Delphi is the first to provide a single system combining 76Ghz Radar and Vision Sensing. With a compact design the system can be integrated behind the windshield and enables a broad array of active safety features, as lane tracking, collision avoidance or adaptive cruise control.
The visual detection is performed by a 1/3” CMOS Image Sensor supplied by a leader in the CIS automotive industry. The sensor is surmounted by a specific 7-lens module and the Mobileye EyeQ3 SoC is used for video processing. Concerning the radar function, Receiver and Transmitter chips from Infineon using SiGe HBT technology are assembled by wire bonding on the RF Board. The Antenna board uses a PTFE-based substrate and is equipped with planar antennas for transmission and reception of the RF signals.
Based on a complete teardown analysis of the Delphi RACam, the report provides the bill-of-material (BOM) and the manufacturing cost of the system. The report also includes analysis of the Image Sensor and Lens module. A structural analysis, with a comparison with Bosch MRR1, highlights the technical choices in RF design made by Delphi.
A physical analysis and manufacturing cost estimation of the Infineon RF chips is available in a separate report, which also includes a comparison with MMICs used in the Bosch MRR1 Radar.
More information on that report at http://www.i-micronews.com/reports.html
New Receiver & Transmitter components with a SiGe:C HBT technology from Infineon
The new integrated Radar and Camera (RACam) 76GHz automotive radar from Delphi integrates receiver and transmitter components from Infineon. These components use the latest SiGe:C HBT technology.
The two bare dies RF component are developed and manufactured by Infineon. The two chips are integrated in the RF board with specific thermal management. The dies have wire bonding and are directly connected to microstrip line transmission which lead the signal to antenna via a specific way through the PCB.
The RF transistors are the newest generation SiGe:C HBT dies from Infineon. The circuits use advanced insulation structure and modern copper metal layers.
The report includes a complete physical analysis of the dies, with details on technical choices regarding the design and the manufacturing process. The SiGe HBT are equally analyzed.
Panasonic PGA26C09DV 600V GaN HEMT teardown reverse costing report published ...Yole Developpement
Panasonic’s first 600V GaN HEMT has an innovative structure designed to integrate a normally-off transistor in a standard package, without a cascade structure
System Plus Consulting unveils Panasonic’s first GaN HEMT, assembled in a standard TO220 package. Thanks to its specific die design, the component is normally-off without using a cascade structure or special packaging.
Panasonic’s PGA26C09DV features a medium-voltage breakdown of 600V for a current of 15A (25°C), with very low RdsOn compared to its competitors. The transistor is optimized for AC-DC power supply, photovoltaic, and motor inverters.
The GaN and AlGaN layers are deposited by epitaxy on a silicon substrate. A complex buffer and template layer structure is used to reduce stress and dislocation. This is complemented by a thick superlattice structure clearly visible in the TEM analysis.
Based on a complete teardown analysis, this report also provides a production cost estimate for the epitaxy, HEMT, and package.
Moreover, this report offers a comparison with GaN Systems’ GS66504B and Transphorm’s GaN HEMT, highlighting the huge differences in design and manufacturing process and their impact on device size and production cost.
For more information visite us at: http://www.i-micronews.com/reports.html
Sensors and Data Management for Autonomous Vehicles report 2015 by Yole Devel...Yole Developpement
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.
Overview of the BF609 dual-core Blackfin processor series covering main features including the Pipelined Vision Processor including the hardware and software development tools. By Analog Devices
topics covered are ASMGCS, HF transmitters an S-band radar. this ppt is useful for students who are taking summer training at Airports Authority of India.
Digilogic's RTES has been designed to meet the demands for a multi-functional platform where you can test and evaluate radar systems. It offers the User-friendly Scenario Generator that will help the User to simulate the complex scenario, and they do it realistically. Be it a seeker relevant to radar or surveillance, tracking, or radar, simulator is the ideal tool to figure out their performance and reliability under different circumstances.
For the full video of this presentation, please visit:
https://www.edge-ai-vision.com/2020/11/advancing-embedded-vision-for-an-autonomous-world-a-presentation-from-qualcomm/
For more information about edge AI and computer vision, please visit:
https://www.edge-ai-vision.com
Ning Bi, Vice President of Technology at Qualcomm, presents the “Advancing Embedded Vision for an Autonomous World” tutorial at the September 2020 Embedded Vision Summit.
Qualcomm Technologies Inc. has revolutionized smartphones, wearables, PCs, smart homes and more. Now Qualcomm has set its sights on vehicles with solutions leveraging Qualcomm’s 5G connectivity, Qualcomm Computer Vision and Qualcomm AI. In this presentation, you’ll learn how the Qualcomm Snapdragon Automotive Cockpit Platform and Qualcomm Snapdragon Ride Platform will deliver a turbocharged experience in the company’s partners’ vehicles with next-gen autonomous driving features and new experiences for its passengers.
Bi focuses on intelligent cockpit solutions related to biometrics – 3D face authentication, anti-spoofing, 3D face reconstruction, human activity detection and cabin occupancy monitoring, as well as the advancements in ADAS and automated driving. And he shows how Qualcomm’s platforms deliver superior power efficiency and scaling across automotive applications.
Autonomous car based on artificial intelligence which is used by google for replacing drivers in car. Which will leads to the driving into the next phase
This White Paper provides a general overview of various military and commercial radar systems. It also covers some typical measurements on such systems and their components.
Learn more about Radar Component Testing here: https://www.rohde-schwarz.com/solutions/test-and-measurement/aerospace-defense/radar-ew-test/radar-component-testing/radar-component-testing_250800.html
Phased-Array Radar Systems Engineering Bootcamp : TonexBryan Len
Phased-Array Radar Systems Engineering Bootcamp is a 3-day preparing program covering phased array radar standards, most recent innovative advancements, programming, framework investigation, prerequisites, engineering, plan and activity. Analyze significant subsystems and related advancements with masters in those territories.
Who Should Attend:
System Engineers and Designers
Software, Hardware and Testing Engineers
Technical Managers
Technicians
Logistics and Support
Operations
Procurement and Specifications Writing Practitioners
Course Agenda and Topics:
Introduction to Radar Systems
Phased Array Radar Fundamentals
Phased-array Radar Design: Application of Radar Fundamentals
Radar Antenna Architectures
Target Detection
Search and Acquisition Functions
Estimation, Tracking, and Data Association
Target Classification, Discrimination, and Identification
Interference Suppression Techniques
Phased-array Radar Architectures
Fundamental Radar Design Trade-offs
Performance-driven Radar Requirements
Missile Defense Radar Design Considerations
Early Warning Radar Design Considerations
Air Defense Radar Design Considerations
Predicted Performance of Phased-array Radars
Learn more about training objectives, pricing, outline etc.
#PhasedArray #Radar #SystemsEngineering Bootcamp
https://www.tonex.com/training-courses/phased-array-radar-systems-engineering-bootcamp/
A SYSTEM CONCEPT FOR A 3D REAL-TIMEOFDM MIMO RADAR FOR FLYING PLATFORMSNexgen Technology
TO GET THIS PROJECT COMPLETE SOURCE ON SUPPORT WITH EXECUTION PLEASE CALL BELOW CONTACT DETAILS
MOBILE: 9791938249, 0413-2211159, WEB: WWW.NEXGENPROJECT.COM,WWW.FINALYEAR-IEEEPROJECTS.COM, EMAIL:Praveen@nexgenproject.com
NEXGEN TECHNOLOGY provides total software solutions to its customers. Apsys works closely with the customers to identify their business processes for computerization and help them implement state-of-the-art solutions. By identifying and enhancing their processes through information technology solutions. NEXGEN TECHNOLOGY help it customers optimally use their resources.
Updates provided to the D-STOP Business Advisory Council at the 2017 Symposium and Board Meeting: https://ctr.utexas.edu/2018/04/12/d-stop-2017-symposium-archive/
Updates provided to the D-STOP Business Advisory Council at the 2017 Symposium and Board Meeting: https://ctr.utexas.edu/2018/04/12/d-stop-2017-symposium-archive/
Updates provided to the D-STOP Business Advisory Council at the 2017 Symposium and Board Meeting: https://ctr.utexas.edu/2018/04/12/d-stop-2017-symposium-archive/
Updates provided to the D-STOP Business Advisory Council at the 2017 Symposium and Board Meeting: https://ctr.utexas.edu/2018/04/12/d-stop-2017-symposium-archive/
Updates provided to the D-STOP Business Advisory Council at the 2017 Symposium and Board Meeting: https://ctr.utexas.edu/2018/04/12/d-stop-2017-symposium-archive/
Updates provided to the D-STOP Business Advisory Council at the 2017 Symposium and Board Meeting: https://ctr.utexas.edu/2018/04/12/d-stop-2017-symposium-archive/
Updates provided to the D-STOP Business Advisory Council at the 2017 Symposium and Board Meeting: https://ctr.utexas.edu/2018/04/12/d-stop-2017-symposium-archive/
Updates provided to the D-STOP Business Advisory Council at the 2017 Symposium and Board Meeting: https://ctr.utexas.edu/2018/04/12/d-stop-2017-symposium-archive/
Updates provided to the D-STOP Business Advisory Council at the 2017 Symposium and Board Meeting: https://ctr.utexas.edu/2018/04/12/d-stop-2017-symposium-archive/
Updates provided to the D-STOP Business Advisory Council at the 2017 Symposium and Board Meeting: https://ctr.utexas.edu/2018/04/12/d-stop-2017-symposium-archive/
Online platforms are emerging as a powerful mechanism for matching resources to requests. In the setting of freight, the requests arrive from shippers, who have a diverse collection of goods. The resources are supplied by shippers (trucks), and have various physical constraints (driver’s route preferences, carrying capacity, geographic preferences, etc.). Online platforms are emerging that (a) learn the characteristics of shippers and carriers, and (b) efficiently match goods to trucks based on such learning.
Our project will develop algorithms for such online resource allocation. This is a challenging problem, due to the complexity of the learning tasks. Such algorithms can have considerable impact on efficiently using trucking resources.
Through this project, the research team will leverage the computing resources and expertise at UT to develop a “data discovery environment” for transportation data to aid decision-making. Many efforts focus on leveraging transportation data to help travelers make decisions, but less thought has gone into a framework for using big data to help transportation agency staff and decision makers. The team will start by building the DDE for the Central Texas region, in collaboration with the local MPO, the City of Austin, and the local transit agency. Initially, the project will focus on creating more meaning from existing data sources, and as the project progresses, it will grow to include more novel data sources and methods. The data platform will be web-based and part of the research includes not only building the tool but developing appropriate protocols for access and governance.
With changing transportation paradigms, there is significant potential for a shift in the balance between the overall population use of, and reliance on, ridesharing services versus traditional transportation options such as personal car ownership or transit use. This shift could lead to a realignment of the bulk of the responsibility for mobility to private entities and away from individual citizens and public entities. Today, as supplemental to the multitude of transportation options that are available, the availability, or lack thereof, of ridesharing services produces low to minimal risk to the traveling public. However, in a future in which ridesharing is optimally (widely) employed, the current independent nature of ridesharing services will influence wider community transit services. This problem statement explores the effects of new types of transportation on transit through the creation of several plausible future scenarios, and what policy decisions could potentially be made to ensure that transit is optimally employed.
Advanced driver assistance systems (ADAS) are a key technology for improving road safety. But both current and proposed ADAS are limited in important ways. Vision- and lidar-based ADAS performs poorly in heavy rain, snow, or fog. Lack of vehicle situational awareness due to these sensing limitations will unfortunately be the cause of many accidents, including fatalities, for connected and automated vehicles in the years to come. The goal of this research is to develop and test a sensing strategy with robust perception: No blind spots, applicable to all driveable environments, and available in all weather conditions. We believe there are three key requirements for collaborative all-weather sensing:
– Precise vehicle positioning within a common reference frame
– Decimeter-accurate vision and radar mapping
– A means of quantifying the benefits of collaborative sensing
Vehicular radar and communication are the two primary means of using radio frequency (RF) signals in transportation systems. Automotive radars provide high-resolution sensing using proprietary waveforms in millimeter wave (mmWave) bands and vehicular communications allow vehicles to exchange safety messages or raw sensor data. Both the techniques can be used for applications such as forward collision warning, cooperative adaptive cruise control, and pre-crash applications.
Many areas of machine learning and data mining focus on point estimates of key parameters. In transportation, however, the inherent variance, and, critically, the need to understand the limits of that variance and the impact it may have, have long been understood to be important. Indeed, variance and other risk measures that capture the cost of the spread around the mean, are critical factors in understanding how people act. Thus they are critical for prediction, as well as for purposes of long term planning, where controlling risk may be equally important to controlling the mean (the point estimate).
There has been tremendous progress on large scale optimization techniques to enable the solution of large scale machine learning and data analytics problems. Stochastic Gradient Descent and its variants is probably the most-used large-scale optimization technique for learning. This has not yet seen an impact on the problem of statistical inference — namely, obtaining distributional information that might allow us to control the variance and hence the risk of certain solutions.
Investigation and findings on reservation-based intersections and managed lanes
Real-Time Signal Control and Traffic Stability
Congestion on urban arterials is largely centered around intersection control. Traditional traffic signal schemes are limited in their ability to adapt in real time to traffic conditions or by their ability to coordinate with each other to ensure adequate performance. Specifically, there is a tension between adaptivity (as with actuated signals) and coordination through pre-timed signals (signal progression). We propose to investigate whether routing protocols in telecommunications networks can be applied to resolve these problems. Specifically, the backpressure algorithm of Tassiulas & Emphremides (1992) can ensure system stability through decentralized control under relatively weak regularity conditions. It is as yet unknown whether this algorithm can be adapted to traffic signal systems, and if so, what modifications are needed. Traffic systems differ in several significant ways from telecommunication networks: each intersection approach has relatively few queues (lanes) that must be shared among traffic to various definitions. First-in, first-out constraints lead to head-of-line blocking effects, traffic waves move at a much slower speed than data packets, and traffic queues are tightly limited by physical space (finite buffers). Determining whether (and how) the backpressure concept can be adapted to traffic networks requires significant research, and has the potential to dramatically improve signal performance.
Improved Models for Managed Lane Operations
Managed lanes (ML) are increasingly being considered as a tool to mitigate congestion on highways with limited areas for capacity expansion. Managed lanes are dynamically priced based on the congestion level, and can be set either with the objective of maximum utilization (e.g., a public operator) or profit maximization (e.g., a private operator). Optimization models for determining these pricing policies make restrictive assumptions about the layout of these corridors (often a single entrance and exit) or knowledge of traveler characteristics on behalf of the modeler (e.g., distribution of willingness to pay). Developing new models to address these issues would allow for better utilization of these facilities.
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
GridMate - End to end testing is a critical piece to ensure quality and avoid...ThomasParaiso2
End to end testing is a critical piece to ensure quality and avoid regressions. In this session, we share our journey building an E2E testing pipeline for GridMate components (LWC and Aura) using Cypress, JSForce, FakerJS…
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AIVladimir Iglovikov, Ph.D.
Presented by Vladimir Iglovikov:
- https://www.linkedin.com/in/iglovikov/
- https://x.com/viglovikov
- https://www.instagram.com/ternaus/
This presentation delves into the journey of Albumentations.ai, a highly successful open-source library for data augmentation.
Created out of a necessity for superior performance in Kaggle competitions, Albumentations has grown to become a widely used tool among data scientists and machine learning practitioners.
This case study covers various aspects, including:
People: The contributors and community that have supported Albumentations.
Metrics: The success indicators such as downloads, daily active users, GitHub stars, and financial contributions.
Challenges: The hurdles in monetizing open-source projects and measuring user engagement.
Development Practices: Best practices for creating, maintaining, and scaling open-source libraries, including code hygiene, CI/CD, and fast iteration.
Community Building: Strategies for making adoption easy, iterating quickly, and fostering a vibrant, engaged community.
Marketing: Both online and offline marketing tactics, focusing on real, impactful interactions and collaborations.
Mental Health: Maintaining balance and not feeling pressured by user demands.
Key insights include the importance of automation, making the adoption process seamless, and leveraging offline interactions for marketing. The presentation also emphasizes the need for continuous small improvements and building a friendly, inclusive community that contributes to the project's growth.
Vladimir Iglovikov brings his extensive experience as a Kaggle Grandmaster, ex-Staff ML Engineer at Lyft, sharing valuable lessons and practical advice for anyone looking to enhance the adoption of their open-source projects.
Explore more about Albumentations and join the community at:
GitHub: https://github.com/albumentations-team/albumentations
Website: https://albumentations.ai/
LinkedIn: https://www.linkedin.com/company/100504475
Twitter: https://x.com/albumentations
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
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Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
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One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
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A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
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We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
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Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
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GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
3. Overview of RADAR
• RADAR = radio detection and ranging
• Radar is an electromagnetic system for the detection and
range of objects
• Where the reflection of the transmitted waveform is used for
detection and range
• And its range (R) is determined by the equation
/2
out R c
back R c
2R out back R c
2R R c
Transmit Pulse
Receive Pulse
4. Automotive RADAR
• FMCW (Frequency Modulated Continuous Wave)
Frequency of Tx signal is modulated in a linear fashion
Frequency difference (Δf) of reflected signal based on range
Long, Mid, and Short Range RADAR (LRR, MRR, SRR)
Image from www.Altera.com
5. Automotive RADAR Use Cases
Adaptive Cruise Control (ACC)
Blind Spot Detection (BSD)
Collision Mitigation (CM)
Lane Change Assist (LCA)
Advanced Driver
Assistance Systems
(ADAS)
6. Trends in Automotive RADAR
• Focus on Safety
360 degrees vehicle surveillance
Object identification/distinction
Rear-end Crash Avoidance
CAR2X (Car 2 Car and Car 2 Infrastructure Communication)
Image from http://www.wykop.pl/link/2349196//
7. Trends in Automotive RADAR
• Adoption of 77- 81GHz
More reliable and more accurate
Greater capability to distinguish objects with high bandwidth
Smaller footprint (multi mode, multi range)
Image from safecarnews.com
8. Trends in Automotive RADAR
• Autonomous Drive Vehicles
From “Assisted” to “Autonomous”
Sensor Validation and Cross-Check
Sensor Control Unit – RADAR, CAMERA, LIDAR, Ultrasonic
Object identification and Action – Software driven architecture
Image from http://articles.sae.org/10794/
9. Trends in Automotive RADAR
“Essentially, this front end processing processes the –
possibly multiple – incoming FMCW analog channels to
a single digital stream of azimuth/range/velocity tuples.
This data flows into more CPU cores where software,
possibly supported by additional accelerators, attempts,
to infer the presence, location, and nature surrounding
the vehicle.”
- Ralf Reuter
RADAR Systems Engineer at Freescale Semiconductor
From “Cutting Through the Fog- The road ahead for vehicular radar”,
Ron Wilson, Altera Corporation, 2012
10. Low-Cost Modular Measurement
and Control Hardware
Productive Software
Development Tools
Highly Integrated
Systems Platforms
Graphical system design combines graphical
programming software with modular hardware,
leveraging the latest technologies
National Instruments in Automotive RADAR
11. Enable Concurrent Design and Test Flow
Link Research to Deployment for Automotive RADAR
DESIGN
VALIDATE
TEST
For state-of-the-art in automotive radar, I can introduce the use of radar in vehicles today and some adoption trends including fitting in with connected vehicles.. I'll also briefly introduce the radar design process and tools to facilitate these adoption trends.
Optimize RF Product Development with NI and AWR How can real world measurements validate a model behavior and reduce the overall product development cycle time? Is it possible to reduce cycle time for RF product development by your choice of tools and platforms used? This session will provide an update to NI and AWR product integration and introduce how you can use flexible and scalable integrated tools from NI and AWR throughout the product development cycle. It will also share how these combined platforms can use real world measurements in your product development flow to effectively bridge design and test development efforts and realize productivity gains including higher quality designs. This presentation will discuss several use cases including PA Design and Radar Systems Design.
2 main topics I will share today are:
An introduction to Automotive RADAR applications
Some trends in this dynamic area of the automotive market
Lets start with aligning what I mean by RADAR?
Radar is actually an acronym and over time the acronym became a noun… RADAR stands for Radio Detection and Ranging, here detection implies the size and speed of the object and the object can be almost anything… TR is the time by the waveform to travel to the target (object) and return to the transmitter. C is the speed of light.. The division by 2 appears because the two way propagation of the of radar
There are many kinds of RADAR systems. Automotive RADAR use FMCW signals ie Frequency modulated continous wave signals.
As shown here, the transmitted signal is modulated in a linear fashion and the frequency difference of the reflected signal indicates the range.
By varying the power of the TX signal, it is possible to create Long, mid, and short range RADAR systems reaching from 15 m to 300M and beyond.
RADAR is used in several applications in automotives as listed here. Mostly compiling under the ADAS systems for driver assistance capability like ACC, BSD, CM and LCA.
The use of RADAR in automotives is forecasted to increase as shown in the table here with annual revenues increasing to about $4B in 2020.
Moving on to the trends in Automotive RADAR, one key trend is the use of RADAR to increase driver and pedestrian Safety.
One use case in work is to use multiple RADAR sensors to offer a 360 degrees surveillance field around an automotive vehicle as shown here. And link to existing driver assistance capability like lane changing, ACC, that were discussed.
Another key trend is to use high bandwidth RADAR systems for accurate object identification especially pedestrians for collision mitigation and rear-end crash avoidance.
Car 2 Car and Car to Infrastructure communication is heavily in its research phase primarily to aid emergency services get on location faster if the need arrises.
Another key trend is the adoption of the RADAR systems at the 77 to 81GHz frequency to create more reliable and accurate radar systems as several different research studies have shown.
Again a primary drive being safety and the increased capability to distinguish various objects of different sizes. Which becomes increasingly critical in dense areas like cities.
One additional trend is the development of smaller and multi-function radar sensors to offer the next generation driver assistance capability
My final trend to share with you today is the advance Assisted Driver capability to Autonomous Drive Vehicles as shown on the image here. .
The primary efforts lead to linking the existing sensor technology, with RADAR taking a primary role, and building the intelligence to cross-check the information between different sensors before pro-actively executing on a course of action.
The variety of available sensors and benefits of simultaneous use will possibly lead to the creation of Sensor Control Units, much like the Engine Control Unit to ensure a high integrity of data collection from the sensors.
And coupled with software driven architecture to process and analyze the data in real-time to determine subsequent actions.
This point is highlighted in this quote from Ralf Reuter who is at Fresscale and showcases that the reliability and use of these next generation RADAR systems will depend more heavily on the software algorithms and processing capabilities.
This software centric need is a tight fit with National Instruments central paradigm for instrumentation systems called Graphical Systems Design.
For over 30 years, National Instruments Graphical Systems Design approach has combined graphical programming software with modular hardware to enable engineers and scientists to effectively build the instrumentation capabilities they need for their efforts.
And our customers are now pulling National Instruments products into several research projects related to next generation Automotive RADAR systems as an enabler to efficiently bridge the data from hardware based sensors to detailed algorithm design efforts and software intelligence.
And also to ultimately and effectively link their efforts with the NI platform as they go from research to validation and to deployment of their applications.
The key trend here is to develop a design process that links the Design and Test efforts to reduce iterations and development time.
As a summary, I will leave you with this image of where the Automotive RADAR trends are heading.
Towards a vision where each vehicle is an interactive node in a transport system, with its own radar field and autonomous driving capability.