Determining a person’s physical position in a multi-building indoor space using wifi fingerprinting on UJIIndoor Data Set to construct machine learning models.
Integrating Eye Tracking Data with Physiological MeasurementsInsideScientific
In this exclusive webinar sponsored by BIOPAC Systems and SensoMotoric Instruments (SMI), experts present user case studies to demonstrate new research capabilities made possible by the plug & play integration of eye tracking technology with physiological recording systems.
Dr. Meike Mischo provides an overview of eye tracking application types and important considerations pertaining to the integration with physiological measurements such as GSR, HR and ECG. Following, Frazer Findlay presents key considerations in recording and analyzing physiological data, showing a live demonstration of a screen-based study. In closing, Dr. Arnd Rose presents a user case study for mobile eye tracking applications using examples from the study of human factors.
HAND GESTURE BASED HOME AUTOMATION FOR VISUALLY CHALLENGEDijiert bestjournal
Rehabilitation engineering is the application of en gineering sciences and technology to improve the quality of life for the people with disabilitie s. A device is designed for the visually challenged people to aid them in operating the home appliances individually. A Microelectromechanical Systems (MEMS) accelerometer is used to sense the accelerations of a hand in motion in three perpendicular directions th at is (x,y,z) and transmitted to wireless protocol using Radio Frequency (RF). The RF signals transmission frequency is 2.25 GHz. The gesture code templates are already stored in the mi crocontroller at the receiver section. The received gestures and the hand gesture shown by the visually challenged is recognized and compared with the templates stored in the receiver. If the templates match the stored templates,then accordingly the home appliances are controlled .
Determining a person’s physical position in a multi-building indoor space using wifi fingerprinting on UJIIndoor Data Set to construct machine learning models.
Integrating Eye Tracking Data with Physiological MeasurementsInsideScientific
In this exclusive webinar sponsored by BIOPAC Systems and SensoMotoric Instruments (SMI), experts present user case studies to demonstrate new research capabilities made possible by the plug & play integration of eye tracking technology with physiological recording systems.
Dr. Meike Mischo provides an overview of eye tracking application types and important considerations pertaining to the integration with physiological measurements such as GSR, HR and ECG. Following, Frazer Findlay presents key considerations in recording and analyzing physiological data, showing a live demonstration of a screen-based study. In closing, Dr. Arnd Rose presents a user case study for mobile eye tracking applications using examples from the study of human factors.
HAND GESTURE BASED HOME AUTOMATION FOR VISUALLY CHALLENGEDijiert bestjournal
Rehabilitation engineering is the application of en gineering sciences and technology to improve the quality of life for the people with disabilitie s. A device is designed for the visually challenged people to aid them in operating the home appliances individually. A Microelectromechanical Systems (MEMS) accelerometer is used to sense the accelerations of a hand in motion in three perpendicular directions th at is (x,y,z) and transmitted to wireless protocol using Radio Frequency (RF). The RF signals transmission frequency is 2.25 GHz. The gesture code templates are already stored in the mi crocontroller at the receiver section. The received gestures and the hand gesture shown by the visually challenged is recognized and compared with the templates stored in the receiver. If the templates match the stored templates,then accordingly the home appliances are controlled .
Magnetic tracking is one of miscellaneous motion capture methods, and maybe the oldest. However, its working principle is rarely introduced in detail perhaps due to its early adaptation resides in military and medical industry. Due to my interest in VR & animation MoCap, I’ve spent some time digging into the very depth of it and would like to share some non-confidential knowledge of it with you.
In this slide, a short history of magnetic tracking will be visited, followed by its working principle and algorithm simulation. Hope you enjoy it.
If you wanna discuss something in depth with me, please don’t hesitate to contact me via: dibao.wang@gmail.com
The Global Positioning System (GPS) is a satellite-based navigation system that can be used to locate positions anywhere on earth made up of a network of 24 satellites placed into orbit
This is used to track the exact location of a vehicle using GPS tracking systems and give information about the position to concerned person through GSM via SMS.
Magnetic tracking is one of miscellaneous motion capture methods, and maybe the oldest. However, its working principle is rarely introduced in detail perhaps due to its early adaptation resides in military and medical industry. Due to my interest in VR & animation MoCap, I’ve spent some time digging into the very depth of it and would like to share some non-confidential knowledge of it with you.
In this slide, a short history of magnetic tracking will be visited, followed by its working principle and algorithm simulation. Hope you enjoy it.
If you wanna discuss something in depth with me, please don’t hesitate to contact me via: dibao.wang@gmail.com
The Global Positioning System (GPS) is a satellite-based navigation system that can be used to locate positions anywhere on earth made up of a network of 24 satellites placed into orbit
This is used to track the exact location of a vehicle using GPS tracking systems and give information about the position to concerned person through GSM via SMS.
Laser Techniqus Company revolutionizes the rifle barrel and gun tube inspection process with Laser-Based Sensors, BEMIS(TM) Bore Erosion Measurement Inspection System
THIS PPT IS ABOUT MEASUREMENT SYSTEM ANALYSIS.. THIS IS VERY USEFUL FOR PERSON WORKING IN INDUSTRY. IT ALSO TALK ABOUT SIX SIGMA APPROACH FOR EFFECTIVE MEASUREMENT.REPEATIBILITY & REPRODUCIBILITY ARE ALSO WELL EXPLAINED IN THIS PPT.
Adaptive Hyper-Parameter Tuning for Black-box LiDAR Odometry [IROS2021]KenjiKoide1
Adaptive Hyper-Parameter Tuning for Black-box LiDAR Odometry
Kenji Koide, Masashi Yokozuka, Shuji Oishi, and Atsuhiko Banno
Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS2021), pp. 7708-7714, Prague, Czech Republic, Sep., 2021
https://staff.aist.go.jp/k.koide/
Hardware & and software system for comparative analysis of GPS modules and ch...UNITESS
Hardware & and software system for comparative analysis of GPS modules and chip antennas from different manufacturers.
www.unitess.ru
sales@unitess.ru
BELARUS +375 (17) 365-35-28
RUSSIA +7 (495) 975-72-83
VIBER | WHATSAPP | TELEGRAM | WECHAT +375 (44) 715-34-69
For Vehicle testing we offer all instruments and after sale support and services.
OEM Technological Instruments offers you the best technology for your R&D and Testing applications.
Please Feel Free to contact Us for any Inquiry.
Mobile : +91-9810533190
Tel: 0120-4370020
E-mail: mail@oemtesting.com oemtech@sify.com
Overcoming challenges of_verifying complex mixed signal designsPankaj Singh
Efficient and Innovative Digital Mixed-Signal (DMS) verification methodology is required to enable effective verification of RX path of SERDES. This presentation describes the usage of Real value models and Capture -Verify approach to verify complex high speed mixed signal design.
Real value models are the backbone of DMS methodology. Real value models are created for all critical modules in Receive path like Equalizer and Sampler and its associated peripheral modules. It is critical to make sure created models are functionally equivalent to respective designs. This is achieved by verifying each created model with respective designs for all functional modes. While the Real Value models are effective in meeting overcoming the simulation performance bottleneck by achieving 10x faster simulation time; the Nonlinearity factors of the front-end design are not represented accurately in discrete domain real value models for next generation of SerDes Design at very high data rate.
To overcome this problem, a novel approach called ‘capture and verify’ is used for verifying the jitter tolerance and eye parameters. In this approach, waveforms from spice level verification of Equalizer for different functional modes are captured and stored. These stored waveforms are used to generate run time table-based models to accurately represent the analog modules. These run time models are used in top-level simulations along with real value models thereby achieving required goal of simulation performance without compromising on accuracy of results.
The complete Design Verification (DV) environment is developed using UVM-e Methodology. Verification environment contains model for transmitter with all de-emphasis settings along with protocol compliant channels with multiple attenuations. DV infrastructure has hooks to plug-in required channel models to verify SERDES. This verification environment is also capable of verifying the clock data recovery (CDR) path of the design using protocol compliant jitter and Spread-Spectrum Clocking (SSC) stimulus.
The real value modelling bridges the gap between the performance requirements of the simulation and accuracy limitations of design. A significant speed-up in simulation performance is achieved (almost 10X in this case) by replacing with functionally equivalent real value models for mixed signal designs. Usage of Capture and Verify methodology with spice simulation waveforms for critical blocks ensures non-linearity of the next generation high speed SerDes design is well captured in simulations provide complete comprehensive solution for high speed mixed signal designs.
A gas turbine is a combustion engine at the heart of a power plant that can convert natural gas or other liquid fuels to mechanical energy. This energy then drives a generator that produces the electrical energy that moves along power lines to homes and businesses.
Presentation on Condition Based Monitoring for gas turbine engines is discussed briefly.
Track 1 session 3 - st dev con 2016 - smart home and building
Track 4 session 3 - st dev con 2016 - pedestrian dead reckoning
1. October 4, 2016
Santa Clara Convention Center
Mission City Ballroom
Pedestrian Dead-Reckoning (PDR)
for Indoor Positioning
MEMS Sensor Solutions Software Team
2. ST PDR for indoor positioning
Continuous and accurate indoor positioning
Connectivity* Sensors Processing PDR
ST PDR
Algorithm
* Needed only for
initial absolute
positioning
2
3. Components of PDR
Demonstration
Sensor calibration
parameters
Altitude filter outputCarry position
Activity modeTotal distance
Step count
Rich interface APIs to get additional information
Demo implemented as an Android application
PDR trajectory and uncertainty estimates
displayed in real-time
• Continuous sensor calibration monitoring
• Body placement detection
• User activity mode detection
• Step detection, including false step rejection
• Variable stride length model and calibration
• Attitude Filter
• User walking direction determination
• Position update logic
• Error model
4
4. PDR Block Diagram
Accelerometer Gyroscope Magnetometer Pressure Sensor
Calibration monitoring Calibration monitoring
Calibration monitoring
disturbance rejection
Step Detection
Stride length determination
Attitude estimation filter
Carry Position determination
User Heading determination
Altitude, floor change
estimation
PDR Output
Latitude – Longitude - Altitude
User Activity, transport
mode
5
5. PDR Mathematical Process
• Velocity and heading are assumed to be constant during the interval when a
step is taken.
• Navigation equation rewritten as a difference equation with piece-wise linear
approximation.
1],1[1
1],1[1
cosˆ
sinˆ
ttttt
ttttt
sNN
sEE
[Nt, Et] = Current position at time t
[Nt-1, Et-1] = Last position at time t-1
𝑠[t-1, t] = Stride length
ψt-1 = User heading
6
6. Carry Position Determination
• Detects Carry Positions
In Hand, Near Head, Shirt
Pocket, Trouser Pocket, On
Desk, Arm Swing
• User Accelerometer data and
Gyroscope (for higher accuracy)
• Optimized for low power and
always on experience
• Uses Machine Learning based
models to achieve higher
accuracy
7
7. Magnetometer Calibration
• Opportunistic magnetometer calibration with following features
• Minimum action requirement from user (< 8 pattern) and less than 2 s time interval.
• Robust against magnetic anomaly.
• Compensate hard iron and soft iron error (9 parameters: 3 offsets, 3-scale factor, 3 – Soft
iron).
8
8. Accelerometer Calibration
• Algorithm to maintain best estimate
of bias and scale factor error in
accelerometer data.
• Fastest accelerometer calibration
based on change in curvature of a
surface and does not require to
hold the device stationary in
different orientation for 2-3
seconds.
9
9. Run-time Gyro Bias Calibration
• Driven by accelerometer and gyroscope data.
• Gyro bias is estimated with device stationary condition.
• Fast calibration process. Requires very small amount of data (less than 60
samples) at 50 Hz rate.
[deg.]
-1
0
1
2
3
4
5
6
0.24
0.49
0.74
0.99
1.24
1.49
1.74
1.99
2.24
2.49
2.74
2.99
3.24
3.49
3.74
3.99
4.24
4.49
4.74
4.99
5.24
5.49
5.74
5.99
6.24
6.49
6.74
6.99
7.24
7.49
7.74
7.99
8.24
8.49
8.74
8.99
9.24
9.49
9.74
9.99
10.24
10.49
10.74
10.99
11.24
11.49
11.74
11.99
12.24
12.49
12.74
12.99
13.24
13.49
Gbias estimation library output
Gyro Gbias
[sec.]
10
10. Walking Angle
• Attitude fusion filter outputs heading in
sensor frame.
• However, device orientation can be
arbitrary (shirt pocket, arm swing,
trouser pocket).
• Walk angle needed to obtain user
heading. Mathematically, computing
North and East distance requires
• 𝑁𝑡 = 𝑁𝑡−1 + 𝑠𝑡−1cos 𝜓
• 𝐸𝑡 = 𝐸𝑡−1 + 𝑠𝑡−1s𝑖𝑛 𝜓
where
𝜓 = 𝜃 + 𝛼
𝜃 = Device heading from
attitude Kalman filter;
α =walk angle
• Based on the physiological characteristics of pedestrian movement, we use the cyclical
characteristics and statistics of acceleration waveform and features to estimates the
misalignment of device with body motion.
• Most challenging problem for the PDR performance.
α
11
11. Walking angle (Arm Swing test)
Device flipped 180o
Device in same
direction
180o turn
12
12. Configurability of PDR
• Input data: Raw / or calibrated data with minimum sample rate of [50, 50, 25,10] Hz
for Accelerometer, gyroscope, magnetometer, pressure sensor.
• Selection of Raw or Calibrated at time of initialization.
• Modularity: Most of algorithm modules are independent and can run with required
inputs (in terms of sensor data and other inputs such as user Heading need attitude
filter data).
• PDR library can be used to run only a specific algorithm, such as step detection, sensor
calibration, attitude filter. Library can be configured to turn modules ON / off during runtime.
• Scheduling: Each module is responsible for its own scheduling and running
condition.
• Individual module can be disabled /enabled at run time. An algorithm / module execution is not
blocked by other modules. Independent of sensor data acquisition and platform.
13
13. PDR Output
• PDR output
• Displacement in ENU coordinate system
• corresponding confidence/error,
• total distance and number of steps.
• Error is computed by the individual error in different components (step detection,
stride length, user heading) of PDR. Each module outputs results with
corresponding error.
• PDR is able to serve multiple clients (such as integration with other position
technologies, relative harvesting) and doesn’t need to be in sync with PDR
processing cycle.
• PDR will provide the best possible output at the time of request using tag interface.
14
14. PDR Walk test:
Test based on sensor only without map matching
Arm Swing
DEMO Setting:
Phone model: Samsung S6 edge +
Sensor list:
6x LSM6DS3
3x Mag based on GMR
In Hand Trouser Pocket
COEX Mall, Seoul
Performance results: High position accuracy
15
15. MIXC Mall-July 2016:
improvement using Map match
PDR Only
PDR + Map matching
DEMO Setting:
Phone model: Samsung S6 edge +
Sensor list:
6x LSM6DS3
3x Mag based on GMR
Pressure sensor
16
19. PDR Output: APIs
• Rich interface APIs are available to get additional information as per
application requirements
• sensor calibration parameters,
• step count,
• total distance,
• activity mode
• carry position
• attitude filter output
• Integration in Context Hub Runtime Environment (CHRE) Framework.
20
20. Integration of ST PDR in Android Stack
Sensor Driver GNSS Driver WiFi Driver BT Driver
Sensor HAL GPS HAL
Network
Location
Provider
FLP HAL Activity HAL
ST PDR
Library
Google Play Services
Fused Location Provider FLP
Applications
Sensor Manager
21