scholarly journals The Effect of Real-time Headbox Adjustments on Data Quality

2018 ◽  
Vol 11 (1) ◽  
Author(s):  
Pieter Blignaut
Keyword(s):  
Real Time ◽  
High Speed ◽  
Good Accuracy ◽  
Sensor Data ◽  
Cmos Camera ◽  
Camera Model ◽  
Accuracy And Precision ◽  
Human Participants ◽  
The Cost ◽  
Camera Sensor

Following a patent owned by Tobii, the framerate of a CMOS camera can be increased by reducing the size of the recording window so that it fits the eyes with minimum room to spare. The position of the recording window can be dynamically adjusted within the camera sensor area to follow the eyes as the participant moves the head. Since only a portion of the camera sensor data is communicated to the computer and processed, much higher framerates can be achieved with the same CPU and camera.Eye trackers can be expected to present data at a high speed, with good accuracy and precision, small latency and with minimal loss of data while allowing participants to behave as normally as possible. In this study, the effect of headbox adjustments in real-time is investigated with respect to the above-mentioned parameters.It was found that, for the specific camera model and tracking algorithm, one or two headbox adjustments per second, as would normally be the case during recording of human participants, could be tolerated in favour of a higher framerate. The effect of adjustment of the recording window can be reduced by using a larger recording window at the cost of the framerate.

Research on Control Power and Self-Sensing Technology for GMM Self-Sensing Actuator

2010 ◽  
Vol 34-35 ◽  
pp. 1314-1318
Author(s):  
Xin Hua Wang ◽  
Shou Qiang Hu ◽  
Qian Yi Ya ◽  
Shu Wen Sun ◽  
Xiu Xia Cao
Keyword(s):  
Real Time ◽  
High Speed ◽  
Dynamic Balance ◽  
The Self ◽  
Time Dynamic ◽  
Scheme Design ◽  
Control Power ◽  
Sensing Technology ◽  
The Cost ◽  
Micro Controller Unit

Structure and principle of a new kind of diphase opposition giant magnetostrictive self-sensing actuator (SSA for short) is introduced, for which a kind of double outputs high-precision NC stable voltage power is designed. With the method of combining with the hardware design and the software setting, the controllability and reliability of the actuator are greatly improved. And the whole design becomes more reasonable, which saves the cost and improves the practicability. In addition, based on the micro controller unit (MCU) with high-speed control, the scheme design of the real-time separation circuit for dynamic balance signal can effectively identify out and pick up the self-sensing signal which changes from foreign pressure feed back. Then the SSA real-time, dynamic and accurately control is realized. The experiment results show that the voltage power can high-speed and accurately output both output voltages with high current, and that the self-sensing signal decoupling circuit can isolate the self-sensing signals without distortion

scholarly journals A Real-Time Health Monitoring System for Remote Cardiac Patients Using Smartphone and Wearable Sensors

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Priyanka Kakria ◽  
N. K. Tripathi ◽  
Peerapong Kitipawang
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Health Monitoring ◽  
Data Security ◽  
High Speed ◽  
Low Cost ◽  
Wearable Sensors ◽  
Healthcare Services ◽  
Cardiac Patients ◽  
The Cost

Online telemedicine systems are useful due to the possibility of timely and efficient healthcare services. These systems are based on advanced wireless and wearable sensor technologies. The rapid growth in technology has remarkably enhanced the scope of remote health monitoring systems. In this paper, a real-time heart monitoring system is developed considering the cost, ease of application, accuracy, and data security. The system is conceptualized to provide an interface between the doctor and the patients for two-way communication. The main purpose of this study is to facilitate the remote cardiac patients in getting latest healthcare services which might not be possible otherwise due to low doctor-to-patient ratio. The developed monitoring system is then evaluated for 40 individuals (aged between 18 and 66 years) using wearable sensors while holding an Android device (i.e., smartphone under supervision of the experts). The performance analysis shows that the proposed system is reliable and helpful due to high speed. The analyses showed that the proposed system is convenient and reliable and ensures data security at low cost. In addition, the developed system is equipped to generate warning messages to the doctor and patient under critical circumstances.

Kinematic SAMI : a new real-time multi-sensor data assimilation strategy for nonlinear modal identification

2020 ◽  
Vol 21 (4) ◽  
pp. 413
Author(s):  
Adrien Goeller ◽  
Jean-Luc Dion ◽  
Ronan Le Breton ◽  
Thierry Soriano
Keyword(s):  
Real Time ◽  
High Speed ◽  
Natural Frequencies ◽  
Modal Identification ◽  
Modal Testing ◽  
Sensor Data ◽  
Contactless Measurement ◽  
Vibration Level ◽  
Set Up ◽  
Real Time Identification

In many engineering applications, the vibration analysis of a structure requires the set up of a large number of sensors. These studies are mostly performed in post processing and based on linear modal analysis. However, many studied devices highlight that modal parameters depend on the vibration level non linearities and are performed with sensors as accelerometers that modify the dynamics of the device. This work proposes a significant evolution of modal testing based on the real time identification of non linear parameters (natural frequencies and damping) tracked with a linear modal basis. This method, called Kinematic-SAMI (for multiSensors Assimilation Modal Identification) is assessed firstly on a numerical case with known non linearities and secondly in the framework of a classical cantilever beam with contactless measurement technique (high speed and high resolution cameras). Finally, the efficiency and the limits of the method are discussed.

scholarly journals Real-time high-resolution video stabilization using high-frame-rate jitter sensing

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Sushil Raut ◽  
Kohei Shimasaki ◽  
Sanjay Singh ◽  
Takeshi Takaki ◽  
Idaku Ishii
Keyword(s):  
High Resolution ◽  
Real Time ◽  
High Speed ◽  
Image Sequences ◽  
Frame Rate ◽  
Video Stabilization ◽  
Feature Points ◽  
Cmos Camera ◽  
Camera System ◽  
Digital Video Stabilization

AbstractIn this study, the novel approach of real-time video stabilization system using a high-frame-rate (HFR) jitter sensing device is demonstrated to realize the computationally efficient technique of digital video stabilization for high-resolution image sequences. This system consists of a high-speed camera to extract and track feature points in gray-level $$512\times 496$$512×496 image sequences at 1000 fps and a high-resolution CMOS camera to capture $$2048\times 2048$$2048×2048 image sequences considering their hybridization to achieve real-time stabilization. The high-speed camera functions as a real-time HFR jitter sensing device to measure an apparent jitter movement of the system by considering two ways of computational acceleration; (1) feature point extraction with a parallel processing circuit module of the Harris corner detection and (2) corresponding hundreds of feature points at the current frame to those in the neighbor ranges at the previous frame on the assumption of small frame-to-frame displacement in high-speed vision. The proposed hybrid-camera system can digitally stabilize the $$2048\times 2048$$2048×2048 images captured with the high-resolution CMOS camera by compensating the sensed jitter-displacement in real time for displaying to human eyes on a computer display. The experiments were conducted to demonstrate the effectiveness of hybrid-camera-based digital video stabilization such as (a) verification when the hybrid-camera system in the pan direction in front of a checkered pattern, (b) stabilization in video shooting a photographic pattern when the system moved with a mixed-displacement motion of jitter and constant low-velocity in the pan direction, and (c) stabilization in video shooting a real-world outdoor scene when an operator holding hand-held hybrid-camera module while walking on the stairs.

Automatic Traffic Queue-End Identification using Location-Based Waze User Reports

2021 ◽  
pp. 036119812110133
Author(s):  
Yuandong Liu ◽  
Zhihua Zhang ◽  
Lee D. Han ◽  
Candace Brakewood
Keyword(s):  
Real Time ◽  
High Speed ◽  
Recurrent Events ◽  
Clustering Algorithm ◽  
Selection Procedure ◽  
The United States ◽  
Streaming Data ◽  
Data Sources ◽  
Sensor Data ◽  
Temporal Extent

Traffic queues, especially queues caused by non-recurrent events such as incidents, are unexpected to high-speed drivers approaching the end of queue (EOQ) and become safety concerns. Though the topic has been extensively studied, the identification of EOQ has been limited by the spatial-temporal resolution of traditional data sources. This study explores the potential of location-based crowdsourced data, specifically Waze user reports. It presents a dynamic clustering algorithm that can group the location-based reports in real time and identify the spatial-temporal extent of congestion as well as the EOQ. The algorithm is a spatial-temporal extension of the density-based spatial clustering of applications with noise (DBSCAN) algorithm for real-time streaming data with an adaptive threshold selection procedure. The proposed method was tested with 34 traffic congestion cases in the Knoxville,Tennessee area of the United States. It is demonstrated that the algorithm can effectively detect spatial-temporal extent of congestion based on Waze report clusters and identify EOQ in real-time. The Waze report-based detection are compared to the detection based on roadside sensor data. The results are promising: The EOQ identification time of Waze is similar to the EOQ detection time of traffic sensor data, with only 1.1 min difference on average. In addition, Waze generates 1.9 EOQ detection points every mile, compared to 1.8 detection points generated by traffic sensor data, suggesting the two data sources are comparable in respect of reporting frequency. The results indicate that Waze is a valuable complementary source for EOQ detection where no traffic sensors are installed.

Real-Time Target Tracking Based on SOPC

2012 ◽  
Vol 433-440 ◽  
pp. 7035-7041
Author(s):  
Zhong Li ◽  
Jian Guo Mao ◽  
Xue Wang Wu ◽  
Wen Yu Lu ◽  
Guang Min Lu
Keyword(s):  
Target Tracking ◽  
Real Time ◽  
High Speed ◽  
Mean Shift ◽  
Electronic System ◽  
Description Language ◽  
The Real ◽  
Dsp Builder ◽  
Hardware Description ◽  
The Cost

The Mean Shift is an algorithm which is a non-parametric probability density gradient estimation. It can be widely applied to the moving target tracking. This paper presents a target tracking method of mean shift based on SOPC. Through designing the parallel algorithm of the system, to take into account the cost of FPGA hardware and complexity of the model, we improve the real-time of the target tracking. Meanwhile, through modeling based on DSP Builder, instead of preparing FPGA hardware description language code, we reduce system designing complexity and improve the designing efficiency of the electronic system . The results show that the real-time of the target tracking is proportional to the Parallelism of design, and the system can meet with the real-time of high-speed visual tracking.

CMOS Real-Time Image Acquisition and Display System Design Based on FPGA

2014 ◽  
Vol 644-650 ◽  
pp. 4403-4406
Author(s):  
Jian Wei Leng ◽  
Ying Hui Wu
Keyword(s):  
Data Acquisition ◽  
System Design ◽  
Real Time ◽  
High Speed ◽  
Image Acquisition ◽  
Control Unit ◽  
Image Sensor ◽  
Acquisition System ◽  
Sensor Data ◽  
Time Data

Based on characteristics of image acquisition system of high-speed and large-capacity, this paper presents a CMOS Image sensor data acquisition system that is using FPGA Chip as its core processing devices. Data acquisition logic control unit is designed by FPGA. The modular structure of the system design, FIFO, ping-pong and other technology are used in the design process to ensure real-time data acquisition and transmission. FPGA implementation of video acquisition can improve system performance. It also has a strong adaptability and flexibility, and it is easy to design, debug and so on. Through the experiment, we can get a clear image.

scholarly journals Real-time Smartphone Activity Classification Using Inertial Sensors—Recognition of Scrolling, Typing, and Watching Videos While Sitting or Walking

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 655 ◽  
Author(s):  
Sijie Zhuo ◽  
Lucas Sherlock ◽  
Gillian Dobbie ◽  
Yun Sing Koh ◽  
Giovanni Russello ◽  
...  
Keyword(s):  
Real Time ◽  
Inertial Sensors ◽  
Optimal Solution ◽  
Sensor Data ◽  
Measurement Unit ◽  
Time Activity ◽  
Motion Data ◽  
Android App ◽  
Human Participants ◽  
Access Policies

By developing awareness of smartphone activities that the user is performing on their smartphone, such as scrolling feeds, typing and watching videos, we can develop application features that are beneficial to the users, such as personalization. It is currently not possible to access real-time smartphone activities directly, due to standard smartphone privileges and if internal movement sensors can detect them, there may be implications for access policies. Our research seeks to understand whether the sensor data from existing smartphone inertial measurement unit (IMU) sensors (triaxial accelerometers, gyroscopes and magnetometers) can be used to classify typical human smartphone activities. We designed and conducted a study with human participants which uses an Android app to collect motion data during scrolling, typing and watching videos, while walking or seated and the baseline of smartphone non-use, while sitting and walking. We then trained a machine learning (ML) model to perform real-time activity recognition of those eight states. We investigated various algorithms and parameters for the best accuracy. Our optimal solution achieved an accuracy of 78.6% with the Extremely Randomized Trees algorithm, data sampled at 50 Hz and 5-s windows. We conclude by discussing the viability of using IMU sensors to recognize common smartphone activities.

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