Smart car-apollo auto-ppt-v4.3
Mozilla Indonesia presents Open source self-driving car - Friday, February 1, 2019
at Mozilla Community Space Jakarta, Mampang Prapatan.
Eventbrite
This document provides an overview and summary of a presentation on Simultaneous Localization and Mapping (SLAM). It introduces the speaker, Dong-Won Shin, and his background and research in SLAM. The contents of the presentation are then outlined, including an introduction to SLAM, traditional SLAM approaches like Extended Kalman Filter SLAM and FastSLAM, efforts towards large-scale mapping like graph-based SLAM and loop closure detection, modern state-of-the-art systems like ORB SLAM, KinectFusion and Lidar SLAM, and applications of SLAM. Key algorithms in visual odometry, backend optimization, and loop closure detection are also summarized.
Computer vision has received great attention over the last two decades.
This research field is important not only in security-related software but also in the advanced interface between people and computers, advanced control methods, and many other areas.
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/
Tracking is the problem of estimating the trajectory of an object as it moves around a scene. Motion tracking involves collecting data on human movement using sensors to control outputs like music or lighting based on performer actions. Motion tracking differs from motion capture in that it requires less equipment, is less expensive, and is concerned with qualities of motion rather than highly accurate data collection. Optical flow estimates the pixel-wise motion between frames in a video by calculating velocity vectors for each pixel.
This document provides a summary of a professional development short course on ELINT (Electronic Intelligence) Interception and Analysis. The course, taught by Dr. Richard G. Wiley, covers methods for intercepting radar and other non-communication signals, analyzing the signals to determine their functions and capabilities, and practical exercises. Participants receive a textbook on ELINT. The 4-day course outline covers topics like radar fundamentals, receiver types, direction finding techniques, emitter location, pulse analysis, and modern radar waveforms.
Computer vision has received great attention over the last two decades.
This research field is important not only in security-related software, but also in advanced interface between people and computers, advanced control methods and many other areas.
Power Point Presentation on object detection using tensorflow :
TensorFlow™ is an open source software library for high performance numerical computation. Its flexible architecture allows easy deployment of computation across a variety of platforms (CPUs, GPUs, TPUs), and from desktops to clusters of servers to mobile and edge devices. Originally developed by researchers and engineers from the Google Brain team within Google’s AI organization, it comes with strong support for machine learning and deep learning and the flexible numerical computation core is used across many other scientific domains.
The KLT tracker is a classic algorithm for visual object tracking published in 1981. It works by tracking feature points between consecutive video frames using the Lucas-Kanade optical flow method. The KLT tracker is still widely used due to its computational efficiency and availability in many computer vision libraries. However, it is best suited for tracking textured objects and may struggle with uniform textures or large displacements between frames.
This document provides an overview and summary of a presentation on Simultaneous Localization and Mapping (SLAM). It introduces the speaker, Dong-Won Shin, and his background and research in SLAM. The contents of the presentation are then outlined, including an introduction to SLAM, traditional SLAM approaches like Extended Kalman Filter SLAM and FastSLAM, efforts towards large-scale mapping like graph-based SLAM and loop closure detection, modern state-of-the-art systems like ORB SLAM, KinectFusion and Lidar SLAM, and applications of SLAM. Key algorithms in visual odometry, backend optimization, and loop closure detection are also summarized.
Computer vision has received great attention over the last two decades.
This research field is important not only in security-related software but also in the advanced interface between people and computers, advanced control methods, and many other areas.
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/
Tracking is the problem of estimating the trajectory of an object as it moves around a scene. Motion tracking involves collecting data on human movement using sensors to control outputs like music or lighting based on performer actions. Motion tracking differs from motion capture in that it requires less equipment, is less expensive, and is concerned with qualities of motion rather than highly accurate data collection. Optical flow estimates the pixel-wise motion between frames in a video by calculating velocity vectors for each pixel.
This document provides a summary of a professional development short course on ELINT (Electronic Intelligence) Interception and Analysis. The course, taught by Dr. Richard G. Wiley, covers methods for intercepting radar and other non-communication signals, analyzing the signals to determine their functions and capabilities, and practical exercises. Participants receive a textbook on ELINT. The 4-day course outline covers topics like radar fundamentals, receiver types, direction finding techniques, emitter location, pulse analysis, and modern radar waveforms.
Computer vision has received great attention over the last two decades.
This research field is important not only in security-related software, but also in advanced interface between people and computers, advanced control methods and many other areas.
Power Point Presentation on object detection using tensorflow :
TensorFlow™ is an open source software library for high performance numerical computation. Its flexible architecture allows easy deployment of computation across a variety of platforms (CPUs, GPUs, TPUs), and from desktops to clusters of servers to mobile and edge devices. Originally developed by researchers and engineers from the Google Brain team within Google’s AI organization, it comes with strong support for machine learning and deep learning and the flexible numerical computation core is used across many other scientific domains.
The KLT tracker is a classic algorithm for visual object tracking published in 1981. It works by tracking feature points between consecutive video frames using the Lucas-Kanade optical flow method. The KLT tracker is still widely used due to its computational efficiency and availability in many computer vision libraries. However, it is best suited for tracking textured objects and may struggle with uniform textures or large displacements between frames.
This document summarizes a seminar presentation on vehicle-to-vehicle (V2V) communications. V2V allows vehicles to communicate with each other to share information like speed and braking status. The goal is to reduce accidents by extending vehicles' sensing abilities. V2V works using dedicated short range communications and a mesh network topology. Future V2V systems may be able to take control of vehicles in dangerous situations and integrate with autonomous driving. V2V offers benefits like increased comfort and safety, but also challenges like reliability and potential driver distraction. Overall, V2V technology aims to improve the driving experience.
In today's competitive environment, the security concerns have grown tremendously. In the modern world, possession is known to be 9/10'ths of the law. Hence, it is imperative for one to be able to safeguard one's property from worldly harms such as thefts, destruction of property, people with malicious intent etc. Due to the advent of technology in the modern world, the methodologies used by thieves and robbers for stealing has been improving exponentially. Therefore, it is necessary for the surveillance techniques to also improve with the changing world. With the improvement in mass media and various forms of communication, it is now possible to monitor and control the environment to the advantage of the owners of the property
Slide for Multi Object Tracking by Md. Minhazul Haque, Rajshahi University of Engineering and Technology
* Object
* Object Tracking
* Application
* Background Study
* How it works
* Multi-Object Tracking
* Solution
* Future Works
For the full video of this presentation, please visit:
http://www.embedded-vision.com/platinum-members/auvizsystems/embedded-vision-training/videos/pages/may-2016-embedded-vision-summit
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Nagesh Gupta, Founder and CEO of Auviz Systems, presents the "Semantic Segmentation for Scene Understanding: Algorithms and Implementations" tutorial at the May 2016 Embedded Vision Summit.
Recent research in deep learning provides powerful tools that begin to address the daunting problem of automated scene understanding. Modifying deep learning methods, such as CNNs, to classify pixels in a scene with the help of the neighboring pixels has provided very good results in semantic segmentation. This technique provides a good starting point towards understanding a scene. A second challenge is how such algorithms can be deployed on embedded hardware at the performance required for real-world applications. A variety of approaches are being pursued for this, including GPUs, FPGAs, and dedicated hardware.
This talk provides insights into deep learning solutions for semantic segmentation, focusing on current state of the art algorithms and implementation choices. Gupta discusses the effect of porting these algorithms to fixed-point representation and the pros and cons of implementing them on FPGAs.
Object tracking involves tracing the movement of objects in a video sequence. There are various object representation methods like points, shapes, and skeletons. Popular tracking algorithms include point tracking, kernel tracking, and silhouette tracking. Key steps are object detection, feature extraction, segmentation, and tracking. Common challenges are illumination changes, occlusions, and complex motions. The document compares methods like optical flow, mean shift, and feature-based tracking. In conclusion, object tracking has advanced but challenges remain like handling occlusions.
Deep Learning Hardware: Past, Present, & FutureRouyun Pan
Yann LeCun gave a presentation on deep learning hardware, past, present, and future. Some key points:
- Early neural networks in the 1960s-1980s were limited by hardware and algorithms. The development of backpropagation and faster floating point hardware enabled modern deep learning.
- Convolutional neural networks achieved breakthroughs in vision tasks in the 1980s-1990s but progress slowed due to limited hardware and data.
- GPUs and large datasets like ImageNet accelerated deep learning research starting in 2012, enabling very deep convolutional networks for computer vision.
- Recent work applies deep learning to new domains like natural language processing, reinforcement learning, and graph networks.
- Future challenges include memory-aug
LIDAR is a remote sensing technology that uses lasers to measure properties of scattered light. It measures the time delay between the transmitted and reflected signals to determine the distance to an object. LIDAR consists of a laser transmitter, receiver, and detector. It uses much shorter wavelengths than RADAR, allowing for higher resolution. Common applications of LIDAR include topographic mapping, atmospheric research, robotics, archaeology, geology, and astronomy.
The document discusses object tracking in computer vision. It begins with an introduction and overview of applications of object tracking. It then discusses object representation, detection, tracking algorithms and methodologies. It compares different tracking methods and provides an example of object tracking in MATLAB. Key steps in object tracking include object detection, tracking the detected objects across frames using algorithms like point tracking, kernel tracking and silhouette tracking. Common challenges with object tracking are also summarized.
The document discusses automotive radar systems, including how they use radio signals to detect objects at a distance and help features like adaptive cruise control. It covers the components of a radar system like the antenna and processing unit, how radar detects objects through timing signals, and applications in driver assistance systems. Radar systems are becoming more important for advanced features in self-driving cars.
- Image classification involves training a classifier on labeled images, validating hyperparameters, and testing on unlabeled images.
- Nearest neighbor classification predicts labels of nearest training examples while linear classification learns weights to separate classes with a hyperplane.
- Loss functions like cross-entropy measure how well the classifier's predicted scores match the true labels and are minimized during training.
AI has three main purposes in medicine: to assist doctors by improving accuracy and speed of decisions, augmenting professionals with expertise, and managing administrative tasks. The two main applications of AI are virtual (machine learning algorithms that improve through experience) and physical (medical devices and robots). Successful medical AI requires addressing challenges like integration issues, data privacy and a lack of standards, while providing benefits like reducing repetitive tasks.
Radar is a system that uses radio waves to detect objects by transmitting electromagnetic waves and analyzing the reflected signals. It consists of a transmitter that generates radio waves, a receiver to detect the reflected waves, and an antenna to transmit and receive the signals. Radar can determine attributes of detected objects such as range, angle, or velocity. It has numerous military and civilian applications including air traffic control, weather monitoring, vehicle speed detection, and space exploration. The Indian Army employs various radar systems like the Rohini, Rajendra, Indra, and Swordfish radars to detect threats. Radar remains an important detection technology due to its all-weather capabilities and ability to sense objects day or night through cloud cover.
The document discusses human action recognition using spatio-temporal features. It proposes using optical flow and shape-based features to form motion descriptors, which are then classified using Adaboost. Targets are localized using background subtraction. Optical flows within localized regions are organized into a histogram to describe motion. Differential shape information is also captured. The descriptors are used to train a strong classifier with Adaboost that can recognize actions in testing videos.
This document provides an introduction to electronic warfare analyses. It discusses definitions of ELINT and EW terminology. It also covers topics like ELINT collection cycles, RF receiver characteristics, direction finding analysis, scan pattern analysis, and PRI analysis. The document puts these concepts together using examples of ESM concepts of operations and potential future ELINT threats that use techniques like LPI, frequency hopping, and spread spectrum.
This document discusses and compares different methods for deep learning object detection, including region proposal-based methods like R-CNN, Fast R-CNN, Faster R-CNN, and Mask R-CNN as well as single shot methods like YOLO, YOLOv2, and SSD. Region proposal-based methods tend to have higher accuracy but are slower, while single shot methods are faster but less accurate. Newer methods like Faster R-CNN, R-FCN, YOLOv2, and SSD have improved speed and accuracy over earlier approaches.
RADAR - RAdio Detection And Ranging
This is the Part 2 of 2 of RADAR Introduction.
For comments please contact me at solo.hermelin@gmail.com.
For more presentation on different subjects visit my website at http://www.solohermelin.com.
Part of the Figures were not properly downloaded. I recommend viewing the presentation on my website under RADAR Folder.
A presentation by John Kenney of Toyota InfoTechnology Center on Apr 9 2019 to the Silicon Valley Automotive Open Source Group: https://www.meetup.com/Silicon-Valley-Automotive-Open-Source/events/259384384/
Fullstop.ai is a level 2 autonomous hardware and software solution by Synergy Robotics and UMA Robotics, with Nvidia Hardware, with Provable ML, using a provable algorithm for a leader follower algorithm based , autonomous navigation system, incorporating Nvidia Jetson SDK, Drive OS, and smart Cities SDK. Similar to Comma.ai
Marek Jersak «Autonomous Drive – From Sensors to Motion».LogeekNightUkraine
Marek Jersak is the Senior Director of Autonomous Drive at Luxoft. The document discusses several challenges facing the autonomous vehicle industry and potential solutions. It notes that developing fully autonomous vehicles will require handling large amounts of data from sensors and overcoming issues like unpredictable human drivers. Luxoft aims to help customers address these challenges through approaches like automated data labeling, developing safety-critical software, and designing systems for teleoperated driving and vehicle security.
This document summarizes a seminar presentation on vehicle-to-vehicle (V2V) communications. V2V allows vehicles to communicate with each other to share information like speed and braking status. The goal is to reduce accidents by extending vehicles' sensing abilities. V2V works using dedicated short range communications and a mesh network topology. Future V2V systems may be able to take control of vehicles in dangerous situations and integrate with autonomous driving. V2V offers benefits like increased comfort and safety, but also challenges like reliability and potential driver distraction. Overall, V2V technology aims to improve the driving experience.
In today's competitive environment, the security concerns have grown tremendously. In the modern world, possession is known to be 9/10'ths of the law. Hence, it is imperative for one to be able to safeguard one's property from worldly harms such as thefts, destruction of property, people with malicious intent etc. Due to the advent of technology in the modern world, the methodologies used by thieves and robbers for stealing has been improving exponentially. Therefore, it is necessary for the surveillance techniques to also improve with the changing world. With the improvement in mass media and various forms of communication, it is now possible to monitor and control the environment to the advantage of the owners of the property
Slide for Multi Object Tracking by Md. Minhazul Haque, Rajshahi University of Engineering and Technology
* Object
* Object Tracking
* Application
* Background Study
* How it works
* Multi-Object Tracking
* Solution
* Future Works
For the full video of this presentation, please visit:
http://www.embedded-vision.com/platinum-members/auvizsystems/embedded-vision-training/videos/pages/may-2016-embedded-vision-summit
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Nagesh Gupta, Founder and CEO of Auviz Systems, presents the "Semantic Segmentation for Scene Understanding: Algorithms and Implementations" tutorial at the May 2016 Embedded Vision Summit.
Recent research in deep learning provides powerful tools that begin to address the daunting problem of automated scene understanding. Modifying deep learning methods, such as CNNs, to classify pixels in a scene with the help of the neighboring pixels has provided very good results in semantic segmentation. This technique provides a good starting point towards understanding a scene. A second challenge is how such algorithms can be deployed on embedded hardware at the performance required for real-world applications. A variety of approaches are being pursued for this, including GPUs, FPGAs, and dedicated hardware.
This talk provides insights into deep learning solutions for semantic segmentation, focusing on current state of the art algorithms and implementation choices. Gupta discusses the effect of porting these algorithms to fixed-point representation and the pros and cons of implementing them on FPGAs.
Object tracking involves tracing the movement of objects in a video sequence. There are various object representation methods like points, shapes, and skeletons. Popular tracking algorithms include point tracking, kernel tracking, and silhouette tracking. Key steps are object detection, feature extraction, segmentation, and tracking. Common challenges are illumination changes, occlusions, and complex motions. The document compares methods like optical flow, mean shift, and feature-based tracking. In conclusion, object tracking has advanced but challenges remain like handling occlusions.
Deep Learning Hardware: Past, Present, & FutureRouyun Pan
Yann LeCun gave a presentation on deep learning hardware, past, present, and future. Some key points:
- Early neural networks in the 1960s-1980s were limited by hardware and algorithms. The development of backpropagation and faster floating point hardware enabled modern deep learning.
- Convolutional neural networks achieved breakthroughs in vision tasks in the 1980s-1990s but progress slowed due to limited hardware and data.
- GPUs and large datasets like ImageNet accelerated deep learning research starting in 2012, enabling very deep convolutional networks for computer vision.
- Recent work applies deep learning to new domains like natural language processing, reinforcement learning, and graph networks.
- Future challenges include memory-aug
LIDAR is a remote sensing technology that uses lasers to measure properties of scattered light. It measures the time delay between the transmitted and reflected signals to determine the distance to an object. LIDAR consists of a laser transmitter, receiver, and detector. It uses much shorter wavelengths than RADAR, allowing for higher resolution. Common applications of LIDAR include topographic mapping, atmospheric research, robotics, archaeology, geology, and astronomy.
The document discusses object tracking in computer vision. It begins with an introduction and overview of applications of object tracking. It then discusses object representation, detection, tracking algorithms and methodologies. It compares different tracking methods and provides an example of object tracking in MATLAB. Key steps in object tracking include object detection, tracking the detected objects across frames using algorithms like point tracking, kernel tracking and silhouette tracking. Common challenges with object tracking are also summarized.
The document discusses automotive radar systems, including how they use radio signals to detect objects at a distance and help features like adaptive cruise control. It covers the components of a radar system like the antenna and processing unit, how radar detects objects through timing signals, and applications in driver assistance systems. Radar systems are becoming more important for advanced features in self-driving cars.
- Image classification involves training a classifier on labeled images, validating hyperparameters, and testing on unlabeled images.
- Nearest neighbor classification predicts labels of nearest training examples while linear classification learns weights to separate classes with a hyperplane.
- Loss functions like cross-entropy measure how well the classifier's predicted scores match the true labels and are minimized during training.
AI has three main purposes in medicine: to assist doctors by improving accuracy and speed of decisions, augmenting professionals with expertise, and managing administrative tasks. The two main applications of AI are virtual (machine learning algorithms that improve through experience) and physical (medical devices and robots). Successful medical AI requires addressing challenges like integration issues, data privacy and a lack of standards, while providing benefits like reducing repetitive tasks.
Radar is a system that uses radio waves to detect objects by transmitting electromagnetic waves and analyzing the reflected signals. It consists of a transmitter that generates radio waves, a receiver to detect the reflected waves, and an antenna to transmit and receive the signals. Radar can determine attributes of detected objects such as range, angle, or velocity. It has numerous military and civilian applications including air traffic control, weather monitoring, vehicle speed detection, and space exploration. The Indian Army employs various radar systems like the Rohini, Rajendra, Indra, and Swordfish radars to detect threats. Radar remains an important detection technology due to its all-weather capabilities and ability to sense objects day or night through cloud cover.
The document discusses human action recognition using spatio-temporal features. It proposes using optical flow and shape-based features to form motion descriptors, which are then classified using Adaboost. Targets are localized using background subtraction. Optical flows within localized regions are organized into a histogram to describe motion. Differential shape information is also captured. The descriptors are used to train a strong classifier with Adaboost that can recognize actions in testing videos.
This document provides an introduction to electronic warfare analyses. It discusses definitions of ELINT and EW terminology. It also covers topics like ELINT collection cycles, RF receiver characteristics, direction finding analysis, scan pattern analysis, and PRI analysis. The document puts these concepts together using examples of ESM concepts of operations and potential future ELINT threats that use techniques like LPI, frequency hopping, and spread spectrum.
This document discusses and compares different methods for deep learning object detection, including region proposal-based methods like R-CNN, Fast R-CNN, Faster R-CNN, and Mask R-CNN as well as single shot methods like YOLO, YOLOv2, and SSD. Region proposal-based methods tend to have higher accuracy but are slower, while single shot methods are faster but less accurate. Newer methods like Faster R-CNN, R-FCN, YOLOv2, and SSD have improved speed and accuracy over earlier approaches.
RADAR - RAdio Detection And Ranging
This is the Part 2 of 2 of RADAR Introduction.
For comments please contact me at solo.hermelin@gmail.com.
For more presentation on different subjects visit my website at http://www.solohermelin.com.
Part of the Figures were not properly downloaded. I recommend viewing the presentation on my website under RADAR Folder.
A presentation by John Kenney of Toyota InfoTechnology Center on Apr 9 2019 to the Silicon Valley Automotive Open Source Group: https://www.meetup.com/Silicon-Valley-Automotive-Open-Source/events/259384384/
Fullstop.ai is a level 2 autonomous hardware and software solution by Synergy Robotics and UMA Robotics, with Nvidia Hardware, with Provable ML, using a provable algorithm for a leader follower algorithm based , autonomous navigation system, incorporating Nvidia Jetson SDK, Drive OS, and smart Cities SDK. Similar to Comma.ai
Marek Jersak «Autonomous Drive – From Sensors to Motion».LogeekNightUkraine
Marek Jersak is the Senior Director of Autonomous Drive at Luxoft. The document discusses several challenges facing the autonomous vehicle industry and potential solutions. It notes that developing fully autonomous vehicles will require handling large amounts of data from sensors and overcoming issues like unpredictable human drivers. Luxoft aims to help customers address these challenges through approaches like automated data labeling, developing safety-critical software, and designing systems for teleoperated driving and vehicle security.
Marek Jersak. Autonomous Drive – From Sensors to MotionIT Arena
Marek Jersak, Senior Director, Autonomous Drive Practice at Luxoft Automotive
Autonomous Drive – From Sensors to Motion
Dr. Marek Jersak received his Diploma in Electrical Engineering from Aachen University of Technology, Germany in 1997. From 1997 to 1999 he worked as a compiler design engineer for Conexant Systems in Newport Beach, California. He returned to school in 1999 and graduated with a PhD in Real-Time Embedded System Design from the Technical University of Braunschweig, Germany in 2004. Together with his university fellow Kai Richter, in 2005 Marek co-founded Symtavision GmbH in Braunschweig, and in 2013 Symtavision Inc in Michigan, serving as Managing Director respectively President for those companies. Symtavision became a globally recognized leader in Timing Analysis tools and architecture consulting for automotive real- time systems with a focus on chassis, active safety, powertrain, body-control and in-vehicle networking. In February 2016, Marek and Kai sold Symtavision to Luxoft. Marek became director of the newly formed ‘Under the Hood’ practice inside Luxoft Automotive. The practice grew to more than 200 engineers in 1.5 years. At the end of 2017, we repositioned the practice to focus fully on various levels of automated driving, from Level-2 / 3 mass-production ADAS software to architectures and algorithms for Level-4 and ultimately Level-5 autonomous driving. Marek is now fully focused on building the teams, customer relationships and engagement models that enable a seamless, scalable and agile solutions offering from sensors to actuators, spanning co-development with our customers of system and software architectures, algorithms, automotive-grade software, integration, and testing.
Dwika Sudrajat discussed autonomous driving car platforms and requirements. Basic requirements include brake-by-wire, steering-by-wire, and other systems. Hardware includes an industrial PC, sensors like LIDAR and cameras. Software includes the Apollo open source platform from Baidu with perception, planning, and other modules. Autonomous features continue to advance toward fully driverless capability.
RhoMobile Suite v5.5 provides tools to create mobile applications that work across operating systems and devices. It allows developers to build apps once and deploy them to iOS, Android, and Windows Mobile/CE. This release provides support for the latest iOS, Android, and Crosswalk features to improve app performance and capabilities. It also includes fixes for issues with cameras, networking, and building.
Юрий Швалик «Apple and Google are converting car into smartphone?»Anna Shymchenko
Google and Apple have announced plans to integrate their mobile operating systems directly into car head units. This would allow drivers to access smartphone apps and content directly from the car's display screen. While it may revolutionize the automotive user experience, carmakers are concerned about security issues and driver distraction that could arise from integrating open platforms into vehicle systems. The document discusses the benefits and challenges of the new approach.
Smart parking systems aim to improve the driver experience, reduce traffic and pollution, and increase safety and efficiency. The system uses sensors and cameras to detect and identify parked cars, and allows drivers to check in and pay automatically via phone or RF card. A raspberry pi board acts as the coordinator to communicate between the sensor units and parking payment system. Issues like sensor accuracy and detecting non-moving objects were addressed. Future work may include GPS location tracking and integrating additional payment methods. The system is intended to help drivers find parking spots more easily.
The document summarizes the key aspects of Android Lollipop (5.0). It discusses how Android Runtime (ART) replaced Dalvik for improved app performance. Material Design was introduced as the new design language. Features like battery optimizations using Project Volta and improved notifications were also covered. The limitations mentioned were longer installation times and potential battery/heating issues. The future scope discussed further Android development across new devices and form factors.
Design of Image Segmentation Algorithm for Autonomous Vehicle Navigationusing...IJEEE
In the past few years Autonomous vehicles have gained importance due to its widespread applications in the field of civilian and military applications. On-board camera on autonomous vehicles captures the images which need to be processed in real time using the image segmentation algorithm. On board processing of video(frames)in real time is a big challenging task as it involves extracting the information and performing the required operations for navigation.This paper proposes an approach for vision based autonomous vehicle navigation in indoor environment using the designed image segmentation algorithm. The vision based navigation is applied to autonomous vehicle and it is implemented using the Raspberry Pi camera module on Raspberry Pi Model-B+ with the designed image segmentation algorithm. The image segmentation algorithm has been built using smoothing,thresholding, morpho- logical operations, and edge detection. The reference images of directions in the path are detected by the vehicle and accordingly it moves in right or left directions or stops at destination. The vehicle finds the path from source to destination using reference directions. It first captures the video,segments the video(frame by frame), finds the edges in the segmented frame and moves accordingly. The Raspberry Pi also transmits the capture video and segmented results using the Wi-Fi to the remote system for monitoring. The autonomous vehicle is also capable of finding obstacle in its path and the detection is done using the ultrasonic sensors.
Land vehicle tracking system using java on android platformAlexander Decker
This document summarizes an academic article about developing a land vehicle tracking system using Java on the Android platform. The article includes:
- An abstract describing the goals of developing a land vehicle tracking system to address problems from heavy traffic like accidents and empty vehicles.
- Sections discussing using GPS to locate vehicles in real-time, objectives like tracking speed limits, notifying administrators and passengers of stops.
- Diagrams of the system architecture including separate client, server and administrator modules.
- Background on relevant technologies like Android, Java, GPS and how they enable real-time vehicle tracking.
- Applications of vehicle tracking systems in fleet management, theft prevention, and improving productivity and safety.
Android N 7.0 introduces many new features for developers including multi-window support, notifications improvements, compiler changes using Jack and Jill, and enhancements to Doze battery optimizations. The presentation focuses on explaining the hybrid JIT/AOT compilation approach in Android N, changes to the Android runtime moving away from Dalvik to ART, impacts of multiprocess WebView, and how to test applications against Doze restrictions.
Onkar Gulavani seeks a position in embedded hardware and software development based on his master's degree in electrical and computer engineering from Colorado State University. He has over 6 years of experience in automotive embedded systems, including working for Cognizant Technology Solutions on CANoe-based testing of instrument panel clusters for Ford Motor Company. His experience also includes various academic and industry projects related to embedded systems, computer vision, and automotive software development.
The document describes the evolution of a student project to create an autonomous vehicle. The original vision was to create a light control system for cars, but this was modified to build an autonomous race car that could follow a black guide strip. Key steps included planning, executing tasks like assembling the car and developing code, and overcoming roadblocks. Lessons learned included project management skills, interfacing components, and developing control loops. Future work could enhance the control algorithm and add features like automated lighting as originally intended.
Module Consolidation: Combining Safety-Critical Automotive Applications with ...Design World
This webinar discusses combining safety-critical automotive applications with non-critical convenience features on a single module or system-on-chip (SoC). It addresses challenges from increasing vehicle complexity and solutions such as consolidating electronic control units (ECUs) and using complex SoCs. Examples of integrating domains like infotainment, driver information, and advanced driver assistance systems are provided. Options for running AUTOSAR communication stacks on external microcontrollers, Linux, or internal processor cores are also examined.
Geogad is a mobile tour guide app that provides location-based historical and local information to travelers using their mobile devices. It leverages mobile web, GPS, and location services to target relevant content and ads based on a user's location and interests. The company CEO Georgi Dagnall believes "cloud" computing is a paradigm shift that allows users to access shared resources over the internet without owning the physical infrastructure.
This session will provide an overview of the new Qualcomm® Snapdragon™ Automotive Development Platform (ADP), which offers the multiple, integrated capabilities of optimized Qualcomm Technologies, Inc., production-grade solutions in a single-board platform. The ADP enables rapid development, testing and deployment of next-generation infotainment apps and experiences for the emerging connected car opportunity. Qualcomm Snapdragon is a product of Qualcomm Technologies, Inc.
Watch this presentation on YouTube:
https://www.youtube.com/watch?v=RMF3AQon3NU
Dolby Atmos technology allows for immersive surround sound from compact speaker systems. It represents sounds as independent objects that can be precisely positioned, including overhead. This allows for an immersive experience from stereo or small multi-channel systems. Dolby Atmos is being adapted for compact configurations like 2.1.2 or 3.1.2 systems using upward-firing speakers or add-on modules to provide overhead effects without a full overhead speaker setup. The document discusses designs for Dolby Atmos compatible compact systems and components.
Dolby Atmos is an object-based audio format that allows sounds to be precisely positioned in 3D space, including overhead. It is becoming widely available in movies, streaming media, games, and home theater systems. Dolby has developed technologies to deliver an immersive Dolby Atmos experience from sound bars, including upward-firing drivers and virtualization processing. Setup guidelines ensure the overhead audio is properly reproduced, such as placing the sound bar at ear level with a clear path to the ceiling.
The document is a user guide for the DLI Atomic Pi that provides an overview of the device's features and interfaces. It includes sections that describe the GPIO pins and how to access them from Linux, Node.js, and Python. It also references the onboard BNO055 sensor and how to interface with it. The document provides information on configuring custom I2C and SPI buses using kernel modules and configuration files. It concludes with details on obtaining technical support and accessing open source code related to the Atomic Pi.
The document provides instructions for setting up and using an Atomic Pi single board computer. It outlines key points such as using the correct power supply to avoid damaging the board, how to connect a monitor and keyboard, and tips for installing and using different operating systems. Troubleshooting advice is also given for issues like boot errors, noise on the keyboard, crashes, and how to increase audio output power.
This document provides a schematic showing the pin connections and mappings for a 26 pin connector. It lists the schematic name, signal name, post-buffer name, atom pin number, bank pin number, driver pin number, and Intel GPIO pin for each connection. Ground and various power connections like +5V, +12V are also indicated.
The document provides an overview and technical specifications of the DLI Atomic Pi single board computer, including:
- Interfaces such as HDMI, audio, USB, Ethernet, and 6 user-configurable GPIO pins.
- Reference information on the GPIO pins and their connections to devices like LEDs.
- Details on controlling the GPIO pins from Linux, Node.js, and Python.
- Specifications of the onboard BNO055 sensor connected via I2C, and code examples for reading sensor data.
- Information on customizing the I2C bus configuration.
Dwika Sudrajat is a managing consultant and CEO of VIDE Freeman Enterprise based in Florida, California, Hong Kong, and Jakarta. He has over 18 years of experience doing in-house training and seminars for various organizations in Indonesia and other countries. He has trained over 1,900 people and his clients include major banks, companies in various industries. Dwika Sudrajat currently holds positions as director, consultant, speaker and guest lecturer. He maintains an active online presence through his blog and social media.
7 million Indonesian university graduates were unemployed in 2016 due to a mismatch between their skills and employer needs. 80% of companies in Indonesia had difficulty finding graduates with the right qualifications, such as soft skills, critical thinking, and digital skills. Speakers at a conference discussed solutions like improving students' skill development during their studies and making education more aligned with career opportunities.
This document compares traditional project management and Scrum methodology. It discusses how Saab used Scrum to develop the Gripen fighter jet software and hardware teams together, while Lockheed Martin used traditional project management to develop the F-35 fighter jet. The document then outlines the core values of Scrum, defines the role of a Scrum Master, and describes the five Scrum ceremonies of backlog grooming, sprint planning, daily scrums, sprint reviews, and retrospectives. It provides an example of running a 90 day Agile framework with sprints, grooming, planning, development, and reviews to create a potentially shippable product at the end.
1 Build Open Source Car Scrum - Dwika V1.pptxDwika Sudrajat
The document outlines an agenda for building a car using Scrum, including planning the design and module slicing, analyzing specifications, continuous development and integration, testing with a preparation checklist and evaluation results, and potentially shipping products. It discusses the benefits of Scrum for agile development including creating solutions to build a better world, scalable products, reducing costs and delivering on time.
Mobil Otonom untuk Mahasiswa - Dwika v3.pptxDwika Sudrajat
An autonomous car was developed for students by Dwika Sudrajat of VIDE Freeman Enterprise and California Research Development. The document outlines the autonomous car's basic functions, autonomous system, how computers see the world, details of the car, the autonomous car lab, wiring of the car, end-to-end implementation, and testing of the smart car.
Dwika Sudrajat is the CEO of VIDE Freeman Enterprise which has offices in Florida, California, Hong Kong, and Jakarta. He has trained over 1,900 people through 18 in-house trainings and 8 organizations in Indonesia. His clients include banks, capital markets, insurance, oil and gas, IT, manufacturing, transportation, construction, and telecommunications companies in USA, China, Hong Kong, Indonesia, Netherlands, Canada, Singapore, UK, and Chile. He also runs coaching programs on Facebook and Yahoo groups with 700,000 listeners in Indonesia and other countries. Currently he holds positions as Director of VIDE Freeman Enterprise, Scrum Master, Business Consultant, SME Coach, and international speaker
Communications-based train control (CBTC) is a railway signaling system that uses telecommunications between the train and track equipment for traffic management and infrastructure control.
Defense and Military Strategy with Agile - ScrumDwika Sudrajat
The document discusses Agile project management using the Scrum framework. It describes key Scrum practices like daily stand-up meetings, prioritizing a backlog of requirements into sprints, and demonstrating progress through sprint reviews and burn down charts. It compares Scrum to traditional waterfall approaches, noting how Scrum allows for more flexibility and ability to manage risk.
This presentation by OECD, OECD Secretariat, was made during the discussion “Competition and Regulation in Professions and Occupations” held at the 77th meeting of the OECD Working Party No. 2 on Competition and Regulation on 10 June 2024. More papers and presentations on the topic can be found at oe.cd/crps.
This presentation was uploaded with the author’s consent.
This presentation by Professor Alex Robson, Deputy Chair of Australia’s Productivity Commission, was made during the discussion “Competition and Regulation in Professions and Occupations” held at the 77th meeting of the OECD Working Party No. 2 on Competition and Regulation on 10 June 2024. More papers and presentations on the topic can be found at oe.cd/crps.
This presentation was uploaded with the author’s consent.
This presentation by Tim Capel, Director of the UK Information Commissioner’s Office Legal Service, was made during the discussion “The Intersection between Competition and Data Privacy” held at the 143rd meeting of the OECD Competition Committee on 13 June 2024. More papers and presentations on the topic can be found at oe.cd/ibcdp.
This presentation was uploaded with the author’s consent.
This presentation by Thibault Schrepel, Associate Professor of Law at Vrije Universiteit Amsterdam University, was made during the discussion “Artificial Intelligence, Data and Competition” held at the 143rd meeting of the OECD Competition Committee on 12 June 2024. More papers and presentations on the topic can be found at oe.cd/aicomp.
This presentation was uploaded with the author’s consent.
This presentation by OECD, OECD Secretariat, was made during the discussion “The Intersection between Competition and Data Privacy” held at the 143rd meeting of the OECD Competition Committee on 13 June 2024. More papers and presentations on the topic can be found at oe.cd/ibcdp.
This presentation was uploaded with the author’s consent.
This presentation by OECD, OECD Secretariat, was made during the discussion “Artificial Intelligence, Data and Competition” held at the 143rd meeting of the OECD Competition Committee on 12 June 2024. More papers and presentations on the topic can be found at oe.cd/aicomp.
This presentation was uploaded with the author’s consent.
The importance of sustainable and efficient computational practices in artificial intelligence (AI) and deep learning has become increasingly critical. This webinar focuses on the intersection of sustainability and AI, highlighting the significance of energy-efficient deep learning, innovative randomization techniques in neural networks, the potential of reservoir computing, and the cutting-edge realm of neuromorphic computing. This webinar aims to connect theoretical knowledge with practical applications and provide insights into how these innovative approaches can lead to more robust, efficient, and environmentally conscious AI systems.
Webinar Speaker: Prof. Claudio Gallicchio, Assistant Professor, University of Pisa
Claudio Gallicchio is an Assistant Professor at the Department of Computer Science of the University of Pisa, Italy. His research involves merging concepts from Deep Learning, Dynamical Systems, and Randomized Neural Systems, and he has co-authored over 100 scientific publications on the subject. He is the founder of the IEEE CIS Task Force on Reservoir Computing, and the co-founder and chair of the IEEE Task Force on Randomization-based Neural Networks and Learning Systems. He is an associate editor of IEEE Transactions on Neural Networks and Learning Systems (TNNLS).
Suzanne Lagerweij - Influence Without Power - Why Empathy is Your Best Friend...Suzanne Lagerweij
This is a workshop about communication and collaboration. We will experience how we can analyze the reasons for resistance to change (exercise 1) and practice how to improve our conversation style and be more in control and effective in the way we communicate (exercise 2).
This session will use Dave Gray’s Empathy Mapping, Argyris’ Ladder of Inference and The Four Rs from Agile Conversations (Squirrel and Fredrick).
Abstract:
Let’s talk about powerful conversations! We all know how to lead a constructive conversation, right? Then why is it so difficult to have those conversations with people at work, especially those in powerful positions that show resistance to change?
Learning to control and direct conversations takes understanding and practice.
We can combine our innate empathy with our analytical skills to gain a deeper understanding of complex situations at work. Join this session to learn how to prepare for difficult conversations and how to improve our agile conversations in order to be more influential without power. We will use Dave Gray’s Empathy Mapping, Argyris’ Ladder of Inference and The Four Rs from Agile Conversations (Squirrel and Fredrick).
In the session you will experience how preparing and reflecting on your conversation can help you be more influential at work. You will learn how to communicate more effectively with the people needed to achieve positive change. You will leave with a self-revised version of a difficult conversation and a practical model to use when you get back to work.
Come learn more on how to become a real influencer!
This presentation by Nathaniel Lane, Associate Professor in Economics at Oxford University, was made during the discussion “Pro-competitive Industrial Policy” held at the 143rd meeting of the OECD Competition Committee on 12 June 2024. More papers and presentations on the topic can be found at oe.cd/pcip.
This presentation was uploaded with the author’s consent.
XP 2024 presentation: A New Look to Leadershipsamililja
Presentation slides from XP2024 conference, Bolzano IT. The slides describe a new view to leadership and combines it with anthro-complexity (aka cynefin).
This presentation by OECD, OECD Secretariat, was made during the discussion “Pro-competitive Industrial Policy” held at the 143rd meeting of the OECD Competition Committee on 12 June 2024. More papers and presentations on the topic can be found at oe.cd/pcip.
This presentation was uploaded with the author’s consent.
Carrer goals.pptx and their importance in real lifeartemacademy2
Career goals serve as a roadmap for individuals, guiding them toward achieving long-term professional aspirations and personal fulfillment. Establishing clear career goals enables professionals to focus their efforts on developing specific skills, gaining relevant experience, and making strategic decisions that align with their desired career trajectory. By setting both short-term and long-term objectives, individuals can systematically track their progress, make necessary adjustments, and stay motivated. Short-term goals often include acquiring new qualifications, mastering particular competencies, or securing a specific role, while long-term goals might encompass reaching executive positions, becoming industry experts, or launching entrepreneurial ventures.
Moreover, having well-defined career goals fosters a sense of purpose and direction, enhancing job satisfaction and overall productivity. It encourages continuous learning and adaptation, as professionals remain attuned to industry trends and evolving job market demands. Career goals also facilitate better time management and resource allocation, as individuals prioritize tasks and opportunities that advance their professional growth. In addition, articulating career goals can aid in networking and mentorship, as it allows individuals to communicate their aspirations clearly to potential mentors, colleagues, and employers, thereby opening doors to valuable guidance and support. Ultimately, career goals are integral to personal and professional development, driving individuals toward sustained success and fulfillment in their chosen fields.
This presentation by Professor Giuseppe Colangelo, Jean Monnet Professor of European Innovation Policy, was made during the discussion “The Intersection between Competition and Data Privacy” held at the 143rd meeting of the OECD Competition Committee on 13 June 2024. More papers and presentations on the topic can be found at oe.cd/ibcdp.
This presentation was uploaded with the author’s consent.
This presentation by Juraj Čorba, Chair of OECD Working Party on Artificial Intelligence Governance (AIGO), was made during the discussion “Artificial Intelligence, Data and Competition” held at the 143rd meeting of the OECD Competition Committee on 12 June 2024. More papers and presentations on the topic can be found at oe.cd/aicomp.
This presentation was uploaded with the author’s consent.
Artificial Intelligence, Data and Competition – ČORBA – June 2024 OECD discus...
Autonomous Driving Car - Open Source
1. Autonomous Driving Car
Open Platform
ApolloAuto
Dwika Sudrajat – Anaheim LA, CA
Scrum Master - Digital Project Manager
VIDE Freeman Corp - California Branch
5. USD 500 Solutions Transform
Vehicle become Semi Autonomous
http://www.autonomix.co/
Low-Cost
3D LiDAR
LIDAR
(Light Detection and Ranging)
Menggunakan pulsa laser.
peraba jarak jauh optik
Mengukur cahaya tersebar
jarak dan informasi lain
dari target yang jauh
6.
7. Lidar /Radar - Deep Learning Cars
Pedestrians - Road signs - Shape of the road
8. Architecture
Hardware/ Vehicle Overview
For Setup:
Hardware:
Industrial PC (IPC)
Global Positioning System (GPS)
Inertial Measurement Unit (IMU)
Controller Area Network (CAN) card
Hard drive
GPS Antenna
GPS Receiver
Software:
Apollo Linux Kernel (based on Linux Kernel 4.4.32)
9.
10. Testing Autonomous Cars 2020: Future Tech - Real World
carmagazine.co.uk
Autonomous Nissan Leaf makes silent, hands-free driving a reality | Drivingdriving.ca
11. Basic Requirements:
Car by-wire system:
Brake by-wire
Steering by-wire
Throttle by-wire
Shift by-wire
(Apollo is Lincoln MKZ)
PC 4-core processor
RAM 6GB min
Ubuntu 14.04
Working knowledge of Docker
36. Apollo Software Overview - Navigation Mode
Copyright and License
Apollo is provided under the Apache-2.0 license.
Disclaimer
Please refer the Disclaimer of Apollo in Apollo's official website.
41. Apollo 3.0's
Focus in a Closed Venue Low-Speed environment.
Lane control, Cruise and Avoid collisions
42. Apollo 2.5
Autonomous run Geo-fenced Highways
with a camera for obstacle detection.
Lane control, Cruise and Avoid collisions.
43. Apollo 2.0 Autonomously Driving on simple urban roads.
Cruise on roads safely, avoid collisions with obstacles, stop at traffic
lights and change lanes..
Modules highlighted in Red are additions or upgrades for version 2.0.
44. Apollo 1.5 for fixed lane cruising.
Addition LiDAR, perception surroundings and map position and plan for
safer maneuvering on its lane.
Yellow are additions or upgrades for version 1.5.
45. modules in Apollo 1.0.
Apollo 1.0:
Apollo 1.0 Automatic GPS Waypoint Following,
works in a test track or parking lot.
This installation is necessary.
46. Apollo Master Upgrade:
The latest Apollo Master Upgrade is capable of navigating
through complex driving scenarios such as residential and
downtown areas.
The car now has 360-degree visibility, along with upgraded
perception algorithms to handle the changing conditions of
urban roads, making the car more secure and aware.
Scenario-based planning can navigate through complex
scenarios including unprotected turns and narrow streets
often found in residential areas and roads with stop signs.
47. Source code in Github
https://github.com/ApolloAuto/apollo
48. .github/ISSUE_TEMPLATE New issue template
5 months ago
.vscode All typos in various documents
20 days ago
cyberframework: fix issue that cyber_recorder play
missing last message
4 days ago
Docker Docker: Try fetching lfs files on dev-start.
an hour ago
Docs Update README.md
23 hours ago
modulestools: record_analyzer added traj stableness
analysis.
an hour ago
Scripts e is not universally understood as an escape
sequence
22 minutes ago
49. third_party update-rss-header-legal-disclaimer
20 days ago
toolsBuild: enabled Wconversion check for all
modules and cyber.
20 days ago
.clang-format Updated clang-format
9 months ago
.gitattributes git-lfs: Migrate large files to lfs.
20 days ago
.gitignore Scripts: Output cyber proto along with
other cyber modules.
20 days ago.
travis.yml Docker: fix yaml-cpp secruity issue with a
patch. (#2328)
20 days
50. AUTHORS.md Update AUTHORS.md
20 days ago
BUILDbuild: use a general USE_GPU compile flag
5 months ago
CONTRIBUTING.md grammar+typos
5 months ago
CPPLINT.cfg Apollo 1.0.0 release
2 years ago
LICENSE Updated license
20 days ago
README.md Docs: updated links
17 days ago
README_cn.md README: updated travis-ci
5 months ago
RELEASE.md All typos in various documents
20 days ago
51. WORKSPACE.in Planning: OpenSpace: BUILD
file for adolc
days ago
apollo.doxygen Doxygen: added README as
front page
year ago
apollo.sh Scripts: Generate cyber python
proto to py_proto.
20 days ago
apollo_docker.sh Tools: Add tool to stat and
expose compile warnings.
20 days ago
52. Autopilot Means Semi-Autonomous Driving
• Autopilot (Software 8.1 up and Hardware 2 up).
• On-Ramp, Off-Ramp— car exit highway by best lane.
• Autosteer — The car’s position is maintained within a lane and allows the driver to briefly
remove their hands from the wheel.
• Smart Summon — car is in tight spaces, car pull out of the parking spot.
• Self-parking— locating a parking spot
• Adaptive Cruise Control — maintaining a safe speeds. can observe car following
• Auto Lane Change— changing lanes on the road, which is guided by the ultrasonic sensors.
• Full Self-Driving Capability — Level 5 is fully self-driving
• Alert System — safety, feature visual and audio alerts to the driver. .
• Collision Detection and Safety— front or side collision with another car, bicycle or pedestrian
of 525 feet
58. Self-driving Uber crash that killed pedestrian in Tempe, Arizona, caught on camera
nbcnews.com Police in Tempe, Ariz. released video from cameras mounted to a self-
driving Uber that struck and killed a pedestrian crossing a busy road.