The aim of this project is to, Analyze and capture driving behaviors that are hazardous, Develop a predictive model for predicting Unsafe Trips and Improve the overall Safety of the ride-hailing services
Sensors Data Processing for Innovative Swimming Tracking DeviceGlobalLogic Ukraine
This presentation is about the development of Instabeat, an innovative swimming tracker, it's sensors, horizon detection and swimming data analysis.
Presentation by Orest Hera (Senior Software Engineer, GlobalLogic, Lviv), delivered at GlobalLogic Lviv Mobile TechTalk, November 13, 2014.
More details -
http://www.globallogic.com.ua/press-releases/lviv-mobile-2014-coverage
This document summarizes a swimming tracker that uses MEMS sensors and machine learning algorithms to detect and analyze swimming motions. It discusses (1) introducing objectives like detecting swimming and resting, counting laps, and recognizing styles while minimizing RAM usage; (2) processing raw sensor data like filtering gravity force from accelerometer data and integrating gyroscope data; and (3) analyzing swimming data by classifying orientation, chains of motion, and inputting data into probabilistic classifiers like expectation-maximization to determine probability distributions of activities.
This presentation is based on the case of development of Instabeat, an innovative swimming tracker. The slides contain hints on how to organize development process, which sensors to use, how to analyze raw data and alternatively use tracking systems.
This presentation by Mykola Shatokhin, GlobalLogic expert, was delivered at GlobalLogic Lviv C++ TechTalk on September 15, 2016. Learn more here: https://www.globallogic.com/ua/gl_news/globallogic-lviv-c-techtalk-summary/
The document describes a fall detection system using a wearable device containing an accelerometer, Arduino Uno, and GPS-GSM module. It proposes using logistic regression on accelerometer data to differentiate between falls and other activities. Features like maximum/minimum acceleration and time between them are extracted. Thresholds of 0.5g for minimum acceleration and 3-8g for maximum acceleration can indicate a fall if their time difference is under 0.8 seconds. Results from daily activities show clear differences in acceleration that could help classify falls. Future work involves integrating the data extraction and model to create a more reliable fall detection algorithm.
Measuring movements of golfers with an accelerometerChangsu Jung
This document discusses designing and implementing an Android application to measure golf swings using an accelerometer. The application aims to provide a simple, convenient measurement system and identify critical points of the swing through acceleration data analysis. It analyzes golf swing data from an accelerometer to detect key points like the backswing, downswing, minimum and maximum peaks. The software architecture includes collecting acceleration data, detecting swing points, and providing audio feedback at different points. Round-down operations simplify the data before detection algorithms identify swing phases and critical timing points.
This document discusses inertial navigation sensor calibration. It provides an introduction to inertial navigation sensors and methods for calibrating accelerometers and gyroscopes. It describes the components of an inertial measurement unit and common calibration methods like the six position static test. Applications of inertial sensors in areas like navigation, tracking, and robotics are also mentioned.
This document describes the development of a mount system called SAVI to mitigate low frequency vibrations from a cryocooler that cause image smearing on a telescope camera. SAVI uses flexures and actuators to sense exported forces and torques up to 20 microns and 60 Hz from the cryocooler. It then computes and actuates to reduce the angular displacement and related pixel smear. Testing showed SAVI was capable of reducing pixel smear by at least 60%, meeting its first performance tier, and had the required compact size and mass.
Sensors Data Processing for Innovative Swimming Tracking DeviceGlobalLogic Ukraine
This presentation is about the development of Instabeat, an innovative swimming tracker, it's sensors, horizon detection and swimming data analysis.
Presentation by Orest Hera (Senior Software Engineer, GlobalLogic, Lviv), delivered at GlobalLogic Lviv Mobile TechTalk, November 13, 2014.
More details -
http://www.globallogic.com.ua/press-releases/lviv-mobile-2014-coverage
This document summarizes a swimming tracker that uses MEMS sensors and machine learning algorithms to detect and analyze swimming motions. It discusses (1) introducing objectives like detecting swimming and resting, counting laps, and recognizing styles while minimizing RAM usage; (2) processing raw sensor data like filtering gravity force from accelerometer data and integrating gyroscope data; and (3) analyzing swimming data by classifying orientation, chains of motion, and inputting data into probabilistic classifiers like expectation-maximization to determine probability distributions of activities.
This presentation is based on the case of development of Instabeat, an innovative swimming tracker. The slides contain hints on how to organize development process, which sensors to use, how to analyze raw data and alternatively use tracking systems.
This presentation by Mykola Shatokhin, GlobalLogic expert, was delivered at GlobalLogic Lviv C++ TechTalk on September 15, 2016. Learn more here: https://www.globallogic.com/ua/gl_news/globallogic-lviv-c-techtalk-summary/
The document describes a fall detection system using a wearable device containing an accelerometer, Arduino Uno, and GPS-GSM module. It proposes using logistic regression on accelerometer data to differentiate between falls and other activities. Features like maximum/minimum acceleration and time between them are extracted. Thresholds of 0.5g for minimum acceleration and 3-8g for maximum acceleration can indicate a fall if their time difference is under 0.8 seconds. Results from daily activities show clear differences in acceleration that could help classify falls. Future work involves integrating the data extraction and model to create a more reliable fall detection algorithm.
Measuring movements of golfers with an accelerometerChangsu Jung
This document discusses designing and implementing an Android application to measure golf swings using an accelerometer. The application aims to provide a simple, convenient measurement system and identify critical points of the swing through acceleration data analysis. It analyzes golf swing data from an accelerometer to detect key points like the backswing, downswing, minimum and maximum peaks. The software architecture includes collecting acceleration data, detecting swing points, and providing audio feedback at different points. Round-down operations simplify the data before detection algorithms identify swing phases and critical timing points.
This document discusses inertial navigation sensor calibration. It provides an introduction to inertial navigation sensors and methods for calibrating accelerometers and gyroscopes. It describes the components of an inertial measurement unit and common calibration methods like the six position static test. Applications of inertial sensors in areas like navigation, tracking, and robotics are also mentioned.
This document describes the development of a mount system called SAVI to mitigate low frequency vibrations from a cryocooler that cause image smearing on a telescope camera. SAVI uses flexures and actuators to sense exported forces and torques up to 20 microns and 60 Hz from the cryocooler. It then computes and actuates to reduce the angular displacement and related pixel smear. Testing showed SAVI was capable of reducing pixel smear by at least 60%, meeting its first performance tier, and had the required compact size and mass.
Measuring Change with Radar Imagery_Richard Goodman - Intergraph Geospatial W...IMGS
The document discusses the importance of measuring change using RADAR imagery. It provides examples of how RADAR has been used to monitor natural disasters like tornadoes, earthquakes, floods, and oil spills. RADAR data is well-suited for change measurement because it can see through clouds and darkness and man-made structures provide strong reflections. Interferometric processing of RADAR data pairs enables coherence change detection and displacement mapping with centimeter accuracy, allowing monitoring of subsidence from activities like resource extraction. The document also describes how ERDAS Imagine software and its radar tools were used by Kongsberg Satellite Services to develop an oil spill and vessel detection system from satellite imagery within 30 minutes of acquisition.
We develop custom Image Recognition systems for Aerospace and defence applications. Using algorithms like Deep Convolutional Neural Networks and Regional Convolutional Neural Networks.
Our algorithms for Target Recognition and Tracking are designed from the beginning to be run on embedded systems. We target both GPU and FPGA devices.
To Train and Validate our algorithms we developed a process to generate photorealistic 3D environments.
Those 3D Environments are used to produce realistic video streams of the targets in different environmental conditions (lighting, adverse meteorological conditions, camouflage, point-of-view).
The same technology can be used to Train and Test Automotive Vision Systems.
This document outlines a project to visually inspect wind turbine blades using drones and artificial intelligence. It defines the problem of creating composite images from drone photos of blades on land and offshore. The proposed solution is to use a cross-correlation algorithm to combine images with 2500px and 3500px overlaps for on-land and offshore blades respectively. The initial results from this algorithm are promising, and future work involves expanding the algorithm to handle vertical shifts and using deep learning on an image database of offshore wind turbines.
The document discusses anomaly detection in spacecraft telemetry. It describes how the German Aerospace Center monitors over 70,000 parameters from spacecraft like Grace Follow-On and TerraSar X for anomalies. It uses techniques like limit checks, novelty detection, and data analysis to identify issues. The ATHMoS system extracts features from historical telemetry to create a model of normal behavior and detects deviations from this model in new data. Future work includes developing multi-parameter anomaly detection and applying quantum algorithms to problems like spacecraft scheduling and secure data transmission.
The document discusses using thermopile sensors and an Extended Kalman Filter (EKF) to estimate the roll angle of a test platform. Thermopile sensors detect infrared radiation and can approximate roll angle through a sine function. An EKF fuses data from two thermopile sensors and an inertial measurement unit (IMU) to estimate roll angle. Testing achieved an average root mean square error of 5.7 degrees between the EKF estimate and IMU measurements. Future work includes applying this to pitch estimation and quadcopter control.
CSIRO has conducted extensive research into automating heavy equipment used in mining operations. This includes developing technologies like dragline swing assist to automate parts of a dragline's operation, digital terrain mapping to aid operators, and autonomous systems for rope shovels, load haul dump vehicles, excavators, and explosive loading. CSIRO is currently working on projects involving automated draglines, wireless sensor networks, and automation of other mining equipment. The research aims to improve safety and productivity through autonomous technologies.
IBM and Google are investing heavily in artificial intelligence and cognitive computing. A new startup is using machine learning and data from existing vehicle sensors to create virtual sensors, including a side slip angle estimator and speed estimator, that do not require additional hardware. These virtual sensors can be used to improve traction control systems and adaptive aerodynamics on performance vehicles. The side slip angle estimator in particular allows for improved traction control and more control over drift for a fun driving experience while maintaining safety.
Traffic Detection Systems (Transportation Engineering)Hossam Shafiq I
This document discusses traffic detection systems. It covers different types of traffic detectors like inductive loops, video, microwave, and infrared detectors. Inductive loops are currently the most popular type of detector. Traffic detectors are important for applications like freeway monitoring, signal control, ramp metering, and traffic enforcement. The document provides examples of loop detectors and video image processors, and gives an overview of how ramp metering uses traffic detection systems.
This document describes a rear vehicle detection system using computer vision techniques for collision avoidance. The system aims to detect vehicles from a mounted camera on a host vehicle and alert the driver of an impending rear-end collision. It utilizes the fast radial symmetry transform to detect the blooming effect of vehicle rear lights under various illumination conditions. Candidate light pairs are identified and their symmetry and distance from the camera are estimated. The system achieves high detection rates on benchmark datasets, including in adverse weather. Future work involves correlating vehicle and braking detection with estimating driver attention level.
1) The document proposes using an embedded stereo camera and fusing optical flow and SIFT feature matching algorithms to estimate the localization of a micro aerial vehicle (MAV) in GPS-denied environments.
2) An Extended Kalman Filter is used to estimate the MAV's translational velocity and altitude from optical flow measurements separated into rotational and translational components using IMU data.
3) Initial experiments fusing optical flow and SIFT matching for altitude estimation showed promising results compared to ground truth, with room for improvement through onboard processing and successive frame SIFT matching for horizontal position estimation.
A force directed approach for offline gps trajectory mapeXascale Infolab
SIGSPATIAL 2018 paper
A Force-Directed Approach for Offline GPS Trajectory Map Matching
Efstratios Rappos (University of Applied Sciences of Western Switzerland (HES-SO)),
Stephan Robert (University of Applied Sciences of Western Switzerland (HES-SO)),
Philippe Cudré-Mauroux (University of Fribourg)
IMU (inertial measurement unit) has already played significant roles in the control system of aerospace and other vehicle platforms. Due to the maturity and low cost of MEMS technology, IMU starts to penetrate consumer products such as smartphone, wearables and VR/AR devices.
This sharing will focus on the general introduction of IMU components, signal characteristics and application concepts, with an attempt to guide those who is interested in the IMU-based system integration and algorithm development.
Evaluation of dynamics | Gyroscope, Accelerometer, Inertia Measuring Unit and...Robo India
Robo India presents theory and working principles of Inertia Measuring unit (IMU), gyroscope, accelerometer and Kalman Filter. It is an important controlling part of unmanned Arial vehicles (UAV)
We have named it as evaluation of dynamics.
We welcome all of your views and queries, we are found at-
website: http://roboindia.com
mail- info@roboindia.com
Semi Autonomous Hand Launched Rotary Wing Unmanned Air Vehiclesahmad bassiouny
The document summarizes research being conducted on semi-autonomous hand-launched rotary-wing unmanned air vehicles (RUAVs) at Penn State. The research aims to design avionics systems using low-cost processors and sensors to enable reliable semi-autonomous control of small electric-powered quad-rotor RUAVs. Several processing and sensor options are being evaluated. Preliminary flight testing is being conducted using a PC-104 system on a Draganflyer III airframe. A custom-designed autopilot board is also being developed and tested. The researchers are taking an incremental approach to control design, starting with stability augmentation and moving towards semi-autonomous and autonomous capabilities.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology
Control Charts in Lab and Trend Analysissigmatest2011
Go through this presentation by Sigma Test and Research Centre and know about control charts in lab and trend analysis. To know more about us visit our website.
Multiple Sensors Soft-Failure Diagnosis Based on Kalman Filtersipij
Sensor is the necessary components of the engine control system. Therefore, more and more work must do for improving sensors reliability. Soft failures are small bias errors or drift errors that accumulate relatively slowly with time in the sensed values that it must be detected because of it can be very easy to be mistaken for the results of noise. Simultaneous multiple sensors failures are rare events and must be considered. In order to solve this problem, a revised multiple-failure-hypothesis based testing is investigated. This approach uses multiple Kalman filters, and each of Kalman filter is designed based on a specific hypothesis for detecting specific sensors fault, and then uses Weighted Sum of Squared Residual (WSSR) to deal with Kalman filter residuals, and residual signals are compared with threshold in order to make fault detection decisions. The simulation results show that the proposed method can be used to detect multiple sensors soft failures fast and accurately.
Credit card fraud is a growing problem that affects card holders around the world. Fraud detection has been an interesting topic in machine learning. Nevertheless, current state of the art credit card fraud detection algorithms miss to include the real costs of credit card fraud as a measure to evaluate algorithms. In this paper a new comparison measure that realistically represents the monetary gains and losses due to fraud detection is proposed. Moreover, using the proposed cost measure a cost sensitive method based on Bayes minimum risk is presented. This method is compared with state of the art algorithms and shows improvements up to 23% measured by cost. The results of this paper are based on real life transactional data provided by a large European card processing company.
An accelerometer is a device that measures acceleration forces, either static (like gravity) or dynamic (caused by movement). It works by measuring the displacement of a damped mass on a spring to determine acceleration. Accelerometers are used in many applications like detecting device orientation, movement analysis, hard drive protection, airbag deployment, and more. The document discusses various types of accelerometers including dual-axis and triple-axis models, and provides examples of interfacing an accelerometer with an AVR microcontroller to control an LCD display based on sensor readings.
The Data Acquisition and Processing Based on MEMS AccelerometerIJRES Journal
This document discusses the design of a data acquisition system using a MEMS accelerometer. It includes:
1) A hardware system is designed using an STM32 microprocessor and ADXL345 accelerometer to acquire acceleration data via I2C communication.
2) Software is developed to read acceleration registers, transmit data to a PC, and receive it using a debugging assistant.
3) Data processing techniques are applied including curve fitting to reduce noise and coordinate transformations to eliminate gravity's effect and calculate motion trajectories using double integration of acceleration data.
4) The acceleration data is simulated in MATLAB to generate the trajectory of the input component as it rotates in 3D space.
Measuring Change with Radar Imagery_Richard Goodman - Intergraph Geospatial W...IMGS
The document discusses the importance of measuring change using RADAR imagery. It provides examples of how RADAR has been used to monitor natural disasters like tornadoes, earthquakes, floods, and oil spills. RADAR data is well-suited for change measurement because it can see through clouds and darkness and man-made structures provide strong reflections. Interferometric processing of RADAR data pairs enables coherence change detection and displacement mapping with centimeter accuracy, allowing monitoring of subsidence from activities like resource extraction. The document also describes how ERDAS Imagine software and its radar tools were used by Kongsberg Satellite Services to develop an oil spill and vessel detection system from satellite imagery within 30 minutes of acquisition.
We develop custom Image Recognition systems for Aerospace and defence applications. Using algorithms like Deep Convolutional Neural Networks and Regional Convolutional Neural Networks.
Our algorithms for Target Recognition and Tracking are designed from the beginning to be run on embedded systems. We target both GPU and FPGA devices.
To Train and Validate our algorithms we developed a process to generate photorealistic 3D environments.
Those 3D Environments are used to produce realistic video streams of the targets in different environmental conditions (lighting, adverse meteorological conditions, camouflage, point-of-view).
The same technology can be used to Train and Test Automotive Vision Systems.
This document outlines a project to visually inspect wind turbine blades using drones and artificial intelligence. It defines the problem of creating composite images from drone photos of blades on land and offshore. The proposed solution is to use a cross-correlation algorithm to combine images with 2500px and 3500px overlaps for on-land and offshore blades respectively. The initial results from this algorithm are promising, and future work involves expanding the algorithm to handle vertical shifts and using deep learning on an image database of offshore wind turbines.
The document discusses anomaly detection in spacecraft telemetry. It describes how the German Aerospace Center monitors over 70,000 parameters from spacecraft like Grace Follow-On and TerraSar X for anomalies. It uses techniques like limit checks, novelty detection, and data analysis to identify issues. The ATHMoS system extracts features from historical telemetry to create a model of normal behavior and detects deviations from this model in new data. Future work includes developing multi-parameter anomaly detection and applying quantum algorithms to problems like spacecraft scheduling and secure data transmission.
The document discusses using thermopile sensors and an Extended Kalman Filter (EKF) to estimate the roll angle of a test platform. Thermopile sensors detect infrared radiation and can approximate roll angle through a sine function. An EKF fuses data from two thermopile sensors and an inertial measurement unit (IMU) to estimate roll angle. Testing achieved an average root mean square error of 5.7 degrees between the EKF estimate and IMU measurements. Future work includes applying this to pitch estimation and quadcopter control.
CSIRO has conducted extensive research into automating heavy equipment used in mining operations. This includes developing technologies like dragline swing assist to automate parts of a dragline's operation, digital terrain mapping to aid operators, and autonomous systems for rope shovels, load haul dump vehicles, excavators, and explosive loading. CSIRO is currently working on projects involving automated draglines, wireless sensor networks, and automation of other mining equipment. The research aims to improve safety and productivity through autonomous technologies.
IBM and Google are investing heavily in artificial intelligence and cognitive computing. A new startup is using machine learning and data from existing vehicle sensors to create virtual sensors, including a side slip angle estimator and speed estimator, that do not require additional hardware. These virtual sensors can be used to improve traction control systems and adaptive aerodynamics on performance vehicles. The side slip angle estimator in particular allows for improved traction control and more control over drift for a fun driving experience while maintaining safety.
Traffic Detection Systems (Transportation Engineering)Hossam Shafiq I
This document discusses traffic detection systems. It covers different types of traffic detectors like inductive loops, video, microwave, and infrared detectors. Inductive loops are currently the most popular type of detector. Traffic detectors are important for applications like freeway monitoring, signal control, ramp metering, and traffic enforcement. The document provides examples of loop detectors and video image processors, and gives an overview of how ramp metering uses traffic detection systems.
This document describes a rear vehicle detection system using computer vision techniques for collision avoidance. The system aims to detect vehicles from a mounted camera on a host vehicle and alert the driver of an impending rear-end collision. It utilizes the fast radial symmetry transform to detect the blooming effect of vehicle rear lights under various illumination conditions. Candidate light pairs are identified and their symmetry and distance from the camera are estimated. The system achieves high detection rates on benchmark datasets, including in adverse weather. Future work involves correlating vehicle and braking detection with estimating driver attention level.
1) The document proposes using an embedded stereo camera and fusing optical flow and SIFT feature matching algorithms to estimate the localization of a micro aerial vehicle (MAV) in GPS-denied environments.
2) An Extended Kalman Filter is used to estimate the MAV's translational velocity and altitude from optical flow measurements separated into rotational and translational components using IMU data.
3) Initial experiments fusing optical flow and SIFT matching for altitude estimation showed promising results compared to ground truth, with room for improvement through onboard processing and successive frame SIFT matching for horizontal position estimation.
A force directed approach for offline gps trajectory mapeXascale Infolab
SIGSPATIAL 2018 paper
A Force-Directed Approach for Offline GPS Trajectory Map Matching
Efstratios Rappos (University of Applied Sciences of Western Switzerland (HES-SO)),
Stephan Robert (University of Applied Sciences of Western Switzerland (HES-SO)),
Philippe Cudré-Mauroux (University of Fribourg)
IMU (inertial measurement unit) has already played significant roles in the control system of aerospace and other vehicle platforms. Due to the maturity and low cost of MEMS technology, IMU starts to penetrate consumer products such as smartphone, wearables and VR/AR devices.
This sharing will focus on the general introduction of IMU components, signal characteristics and application concepts, with an attempt to guide those who is interested in the IMU-based system integration and algorithm development.
Evaluation of dynamics | Gyroscope, Accelerometer, Inertia Measuring Unit and...Robo India
Robo India presents theory and working principles of Inertia Measuring unit (IMU), gyroscope, accelerometer and Kalman Filter. It is an important controlling part of unmanned Arial vehicles (UAV)
We have named it as evaluation of dynamics.
We welcome all of your views and queries, we are found at-
website: http://roboindia.com
mail- info@roboindia.com
Semi Autonomous Hand Launched Rotary Wing Unmanned Air Vehiclesahmad bassiouny
The document summarizes research being conducted on semi-autonomous hand-launched rotary-wing unmanned air vehicles (RUAVs) at Penn State. The research aims to design avionics systems using low-cost processors and sensors to enable reliable semi-autonomous control of small electric-powered quad-rotor RUAVs. Several processing and sensor options are being evaluated. Preliminary flight testing is being conducted using a PC-104 system on a Draganflyer III airframe. A custom-designed autopilot board is also being developed and tested. The researchers are taking an incremental approach to control design, starting with stability augmentation and moving towards semi-autonomous and autonomous capabilities.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology
Control Charts in Lab and Trend Analysissigmatest2011
Go through this presentation by Sigma Test and Research Centre and know about control charts in lab and trend analysis. To know more about us visit our website.
Multiple Sensors Soft-Failure Diagnosis Based on Kalman Filtersipij
Sensor is the necessary components of the engine control system. Therefore, more and more work must do for improving sensors reliability. Soft failures are small bias errors or drift errors that accumulate relatively slowly with time in the sensed values that it must be detected because of it can be very easy to be mistaken for the results of noise. Simultaneous multiple sensors failures are rare events and must be considered. In order to solve this problem, a revised multiple-failure-hypothesis based testing is investigated. This approach uses multiple Kalman filters, and each of Kalman filter is designed based on a specific hypothesis for detecting specific sensors fault, and then uses Weighted Sum of Squared Residual (WSSR) to deal with Kalman filter residuals, and residual signals are compared with threshold in order to make fault detection decisions. The simulation results show that the proposed method can be used to detect multiple sensors soft failures fast and accurately.
Credit card fraud is a growing problem that affects card holders around the world. Fraud detection has been an interesting topic in machine learning. Nevertheless, current state of the art credit card fraud detection algorithms miss to include the real costs of credit card fraud as a measure to evaluate algorithms. In this paper a new comparison measure that realistically represents the monetary gains and losses due to fraud detection is proposed. Moreover, using the proposed cost measure a cost sensitive method based on Bayes minimum risk is presented. This method is compared with state of the art algorithms and shows improvements up to 23% measured by cost. The results of this paper are based on real life transactional data provided by a large European card processing company.
An accelerometer is a device that measures acceleration forces, either static (like gravity) or dynamic (caused by movement). It works by measuring the displacement of a damped mass on a spring to determine acceleration. Accelerometers are used in many applications like detecting device orientation, movement analysis, hard drive protection, airbag deployment, and more. The document discusses various types of accelerometers including dual-axis and triple-axis models, and provides examples of interfacing an accelerometer with an AVR microcontroller to control an LCD display based on sensor readings.
The Data Acquisition and Processing Based on MEMS AccelerometerIJRES Journal
This document discusses the design of a data acquisition system using a MEMS accelerometer. It includes:
1) A hardware system is designed using an STM32 microprocessor and ADXL345 accelerometer to acquire acceleration data via I2C communication.
2) Software is developed to read acceleration registers, transmit data to a PC, and receive it using a debugging assistant.
3) Data processing techniques are applied including curve fitting to reduce noise and coordinate transformations to eliminate gravity's effect and calculate motion trajectories using double integration of acceleration data.
4) The acceleration data is simulated in MATLAB to generate the trajectory of the input component as it rotates in 3D space.
This document summarizes the design, modeling, components, and control strategy of a quadcopter unmanned aerial vehicle. Key aspects include:
1) It uses four propellers powered by brushless DC motors for vertical take-off and landing, with yaw, roll, and pitch control achieved by varying motor speeds.
2) An IMU, microcontroller, and PID controllers provide attitude estimation and motor control.
3) A complementary filter fuses gyroscope and accelerometer data to estimate orientation with drift correction.
4) Wireless transmission of sensor data and live video enable remote control and monitoring of flight.
Knight Gear is an autonomous robot that follows and carries materials for a user. It uses ultrasonic sensors for object detection and avoidance to prevent collisions. An infrared sensor tracks the user within 10 feet. The robot has a maximum payload of 30 pounds, runs for 1 hour on rechargeable batteries, and connects wirelessly within 10 feet. A microcontroller controls the four DC motors and sensors to follow the user. The team aims to easily carry school materials while maintaining posture. Collision detection algorithms allow the robot to navigate safely and stop before impacts.
The document describes the development of a new motionlogger actigraph. It discusses actigraphy technology, uses in sleep and medical research, and key design considerations for motionlogger devices. Specifically, it outlines the importance of low noise, a sensitive accelerometer, precise filtering, avoiding data collection during high current operations, and using a stable power supply to achieve high accuracy compared to polysomnography.
Hardware Implementation of Low Cost Inertial Navigation System Using Mems Ine...IOSR Journals
This document describes the hardware implementation of a low-cost inertial navigation system using MEMS sensors. It discusses:
1) Calibrating the tri-axial accelerometer using a multi-position test to determine nine calibration parameters (scale factors, biases, misalignments) with equations, reducing the number of positions needed from twelve to six.
2) Similarly calibrating the tri-axial gyroscope using rate tests at different rotation speeds.
3) Developing error models for the accelerometer and gyroscope based on the calibrated parameters to remove sensor errors and noise.
4) Implementing the calibration algorithms and navigation equations in a microcontroller to track objects in real-time using the sensor data.
This paper proposes an enhanced method to control the headlight intensity of the vehicle using Ambient Light Sensor (ALS), NI myRIO and LabVIEW. The main function of this automated headlight control is to control the intensity of the headlight based on the ambient light intensity using Pulse Width Modulation (PWM). The intensity of the headlight will be low when the ambient light intensity is high and vice versa.
Position control of a single arm manipulator using ga pid controllerIAEME Publication
This document discusses using a genetic algorithm to optimize the gains of a PID controller for a fixed arm manipulator system. It begins by describing the hardware and software implementation of the fixed arm system, including sensors, actuators, and control interfaces. It then applies a system identification technique to develop a transfer function model of the system based on experimental input/output data. Finally, it uses a genetic algorithm offline to search for the optimal PID gain values that minimize errors for the identified system model. The optimal PID gains found using the genetic algorithm are then applied to the real fixed arm system for control.
This document describes an event data recorder (EDR) system for automobiles. The EDR would record vehicle data during and after an accident using sensors like accelerometers and gyroscopes. If an accident is detected, it would send an alert message with the vehicle's location to pre-stored contacts. This would help provide faster medical assistance. The proposed low-cost EDR system aims to record important vehicle parameters without being too expensive to implement in all vehicles like a traditional "black box". It could help determine the causes of accidents and assist insurance companies and police investigations.
The Application of Gyro in Vehicle Angle MeasurementIJRES Journal
The traditional angle sensor in angle measurement of vehicle has some disadvantages such as low stability, low reliability and anti-interference ability. Based on inertiadeviceshas a high sensitivity and little affected by magnetic interference in the measurement of angle .It does not rely on external information and stability advantages. Using gyroscopes, is developed to replace the traditional method for measuring the angle of angle sensor for vehicle, and random error in measurement results are filtered. The programme uses a CRS03-04S gyro, installed on the vehicle front wheel shaft, used to measure the angular speed of the front wheels, in order to calculate the actual angle of the front wheels. Finally, using Kalman filtering to deal with the random error in measurement results separately, so as to provide accurate real-time information around the corner on the steering control. Experimental results show that thisprogramme than with traditional sensor has better stability, reliability, and timeliness, but also is more accurate.
This document describes the implementation of a QRS detection algorithm from ECG signals using a TMS320C6713 digital signal processor. The algorithm uses bandpass filtering, differentiation, squaring, moving window integration and adaptive thresholding to detect QRS complexes in ECG data. The author tested the algorithm by implementing it in Simulink and then transferring it to the TMS320C6713 DSP platform using Real-Time Workshop and Code Composer Studio. Some software compatibility issues were encountered during implementation on the DSP.
Similar to Improving the safety of ride hailing services using iot analytics (20)
Global Situational Awareness of A.I. and where its headedvikram sood
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataKiwi Creative
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
- - -
This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
End-to-end pipeline agility - Berlin Buzzwords 2024Lars Albertsson
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
Open Source Contributions to Postgres: The Basics POSETTE 2024ElizabethGarrettChri
Postgres is the most advanced open-source database in the world and it's supported by a community, not a single company. So how does this work? How does code actually get into Postgres? I recently had a patch submitted and committed and I want to share what I learned in that process. I’ll give you an overview of Postgres versions and how the underlying project codebase functions. I’ll also show you the process for submitting a patch and getting that tested and committed.
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
2. Project
Overview
The Industry
• Ride-hailing Taxi Service Market to Garner $126.52 Billion by
2025 at 16.5% CAGR.
• Primary reason for such a growth rate being rising trend of on-
demand transportation services, high-end employment
opportunities and lower rate of car ownership among
millennials
The Challenge
• Even since the emergence of these Ride hailing companies,
there has been an increase in 3 to 5 % of accidents.
• Many Research concludes that fleet or company drivers have an
increased crash risk relative to that of privately registered
vehicle drivers.
The Objective
• Analyze and capture driving behaviors that are hazardous
• Develop a predictive model for predicting Unsafe Trips
• Improve the over all Safety of the ride hailing services
3. IOT & Analytics – a
Powerful Combination
• Internet of Things (IOT) –
• By 2025, there will be 116 million IOT enabled cars in the
U.S. And each connected car will upload 25 GB of data per
hour (~ 219 TB / Yr.)
• IOT – a costly piece of technology, is it?
• Well, no it isn’t anymore
• All average smartphones today are equipped with basic
embedded low-cost telemetry sensors such as
accelerometers, gyroscopes, GPS etc.
• Role of IOT Analytics
• Large sets of driving data from GPS and telemetry sensors
allows for exciting new research possibilities using advance
Analytics and Machine Learning techniques
• Insights from IOT Analytics help address key concerns
facing industries
4. IOT Sensors – Accelerometer
& Gyroscope
• What is an Accelerometer?
• Accelerometer sensor reports the acceleration of the device along the 3 sensor
axes (X, Y, Z)
• The measured acceleration includes both the physical acceleration (change of
velocity) and the gravity
• All values are in SI units (m/s^2)
• What is a Gyroscope?
• A gyroscope sensor reports the rate of rotation of the device around the 3 sensor
axes (X, Y, Z)
• Rotations can be of 3 different types
• Pitch – Pitch is for Y axis rotational rate in (rad/s)
• Roll – Roll is for X axis rotational rate in (rad/s)
• Yaw – Yaw is for Z axis rotational rate in (rad/s)
6. Exploratory Data
Analysis
Acceleration X, Acceleration Z,
Gyro Y show a significant
difference when the trip is
classified as Unsafe. These are
initial pointers that these features
may play significant role in
determining the trip type
7. EDA (Contd.)
• Speed
• Lower speed bins have higher
percentage of unsafe rides.
Reasons could be
• Driving below speed limits
• Talking or Texting while driving
• Duration
• Longer durations Trips have
higher percentage of unsafe rides.
Reason could be
• Drowsy driving due to longer driving
hours
• Customers tendency to rate longer
drivers as uncomfortable
• Clearly both Speed and Trip
Duration have a significant role
in classifying a Trip Quality
8. Telemetry Data Processing Methods
• Removing Noise component from Raw Accelerometer and Gyroscope Data using Low Pass Filter
alpha = 0.8
Filter BEGIN
Low_X = alpha × Prev_Low_X + (1 − alpha) × Curr_X
Low_Y = alpha × Prev_Low_Y + (1 − alpha) × Curr_Y
Low_Z = alpha × Prev_Low_Z + (1 − alpha) × Curr_Z
Filter END
• Removing Gravity component from the Accelerometer data
𝑎ℎ𝑜𝑟 = 𝐴𝑐𝑐𝑓𝑖𝑙𝑡𝑒𝑟𝑒𝑑 − 𝐴𝑐𝑐𝑔
Where 𝐴𝑐𝑐𝑔 is the average of acceleration over a window size of 100 seconds
• Calculate Magnitude or Total Acceleration / Total Angular Velocity
𝑎𝑐𝑐𝑅 = 𝑎𝑥2 + 𝑎𝑦2
+ 𝑎𝑧2
𝑎𝑐𝑐𝐻 = 𝑎𝑥ℎ𝑜𝑟
2
+ 𝑎𝑦ℎ𝑜𝑟
2
+ 𝑎𝑧ℎ𝑜𝑟
2
𝑣𝑒𝑙𝑅 = 𝑣𝑥2 + 𝑣𝑦2
+ 𝑣𝑧2
𝑣𝑒𝑙𝐹 = 𝑣𝑥𝑓𝑖𝑙𝑡𝑒𝑟𝑒𝑑
2
+ 𝑣𝑦𝑓𝑖𝑙𝑡𝑒𝑟𝑒𝑑
2
+ 𝑣𝑧𝑓𝑖𝑙𝑡𝑒𝑟𝑒𝑑
2
Where,
ax, ay, az are raw accelerations along x, y, z axis respectively
𝑎𝑥ℎ𝑜𝑟 , 𝑎𝑦ℎ𝑜𝑟 , 𝑎𝑧ℎ𝑜𝑟 are filtered accelerations with gravity treatment done
vx, vy, vz are raw angular velocity along x, y, z axis respectively
𝑣𝑥𝑓𝑖𝑙𝑡𝑒𝑟𝑒𝑑 , 𝑣𝑦𝑓𝑖𝑙𝑡𝑒𝑟𝑒𝑑 . 𝑣𝑧𝑓𝑖𝑙𝑡𝑒𝑟𝑒𝑑 are filtered angular velocity
Data Processing (2)
Noise
Treatment
• (2A) Treating Raw data through LPF
Gravity
Treatment
• (2B) Remove Gravity from
Accelerometer Raw data
Calculate
Magnitude
• (2C) Calculate Magnitude of
Acceleration and of Angular Velocity
10. Significant Driving Events and
Patterns
• STEP 1 – Aggressive Turn Events
• Turn events is based on filtered gyroscope energy (𝑣𝑒𝑙𝐹)
• velF >= 0.025
• STEP 2 – Aggressive Acceleration / Breaking Events
• Acceleration (braking/accelerating) events identification,
from vehicle acceleration component information (accH)
• accH >= 0.002
• STEP 3 – Zig-Zag Events
• Zig-zag events are identified as two or more change lanes
from the significant measurements of the accelerometer
(accH) with very less angular Velocity (velR)
• velF <= 0.0025 & accH >= 0.0013
• STEP 4 – Normal Events
• Any instance that are not classified as one of the above 3 are
classified as normal events
Thresholds For Event Identification
Thresholds are identified using a combination of mean, Outliers and trial
and error method
12. Conclusion &
Recommendations
Predicted Unsafe Rides
• Alerted on real time basis to respective drivers giving them
opportunity to correct it
• Alert the drivers on driving behaviors that are Unsafe
Performance Evaluation
• Drivers can be evaluated based on their driving behaviors thus
encouraging better driving practices among drivers
Organizing Mandatory Trainings
• With specific importance on Feature’s relative importance from
Models
• Going slower that designated speed limits seems to have caused
more Trips unsafe
• Higher Trip Duration tend to be more unsafe. Encourage drivers
to take short / traffic less routes
• Advised to avoid Zig-Zag movements as they cause more trips
unsafe