Comparison of Compensation Mechanism Between an NMR Gyroscope and an SERF Gyroscope

Dong, Haifeng; Gao, Yang

doi:10.1109/jsen.2017.2703601

Cited by 9 publications

(4 citation statements)

References 17 publications

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“…Figure 20 and Figure 21 display the performer's head towards the camera, even when it should be facing away the camera when performing the movement "Walking backwards". An alternative solution to the problem faced by Kinect sensor as suggested by the 3D animation expert is using multiple Kinect sensors to get better motion data or using more advanced motion tracking system such as marker based motion capture system with cameras "Optitrack" (name of a brand) or markered based motion capture system with gyroscope sensors [19] without any camera for a more affordable alternative. This motion capture system with gyroscope is also equipped by accelerometer and magnetometer.…”

Section: Results Analysismentioning

confidence: 99%

Markerless motion capture for 3D human model animation using depth camera

2019

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3D animation is created using keyframe based system in 3D animation software such as Blender and Maya. Due to the long time interval and the need of high expertise in 3D animation, motion capture devices were used as an alternative and Microsoft Kinect v2 sensor is one of them. This research analyses the capabilities of the Kinect sensor in producing 3D human model animations using motion capture and keyframe based animation system in reference to a live motion performance. The quality, time interval and cost of both animation results were compared. The experimental result shows that motion capture system with Kinect sensor consumed less time (only 2.6%) and cost (30%) in the long run (10 minutes of animation) compare to keyframe-based system, but it produced lower quality animation. This was due to the lack of body detection accuracy when there is obstruction. Moreover, the sensor's constant assumption that the performer's body faces forward made it unreliable to be used for a wide variety of movements. Furthermore, standard test defined in this research covers most body parts' movements to evaluate other motion capture system.

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Section: Results Analysismentioning

confidence: 99%

Markerless motion capture for 3D human model animation using depth camera

2019

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“…A gyroscope is utilised to estimate the angular speed and support maintain direction in navigation applications [16,17]; it is typically combined with an accelerometer to construct the inertial navigation system. The main gyroscope frame consists of a gimbal and a rotor, where the spin axis is free to represent any orientations without interference from tilting and rotations.…”

Section: Wearable Sensors: Basic Informationmentioning

confidence: 99%

Multimodal sensing for assisted living using radar

Shrestha

Fioranelli

et al. 2020

Micro-Doppler Radar and Its Applications

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In nature, information from the various senses is utilised by intelligent beings for identification, recognition and decision. It is easier to identify an object or a threat by mixing the auditory, visual and tactile information, than when only one of the senses are used. Combining separate sources of information, which include different types of data, provides the variety needed for cognition and improved situational awareness. This is the rationale behind sensor fusion [1].For deployment in real-world healthcare facilities, either in private homes or in hospital and care homes, activity-monitoring systems with a single sensor may not deliver the necessary robust performance requirements. Radar sensing is an emerging approach in the field of assisted living applications [2,3], but limitations in identifying precise movements, especially at unfavourable aspect angles, and a narrow area of detection, mean further co-operative sensing methods are required for a robust system for detection of movements and falls. Sensor fusion is, therefore, one method of mitigating this issue. This chapter will explore the different sensor and fusion topologies, using active radar sensing in conjunction with other sensing technologies as support for assisted living and healthcare applications [2]. Initially, the sensors and their outputs will be described, with specifics of signal processing of the different sensors and the machine learning for classification detailed. The results of applying these methods to the assisted living scenario will then be presented.This chapter will give insight into activity classification with radar and additional sensing technologies, in particular, wearable inertial and magnetic sensors, focusing on the key information fusion approaches and main improvements using experimental data as validation.

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“…By contrast, the coating performance is mainly measured by the relaxation time of the alkali metal vapor cell through optical quantum detection [14][15][16][17]. Seltzer successfully developed an alkali test cell to test the antirelaxation properties of surface coatings and found that the antirelaxation quality changes as a function of temperature [7].…”

Section: Introductionmentioning

confidence: 99%

Adhesion Effect on the Hyperfine Frequency Shift of an Alkali Metal Vapor Cell with Paraffin Coating Using Peak-Force Tapping AFM

Wei

Wen

et al. 2020

Coatings

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We have investigated the adhesion effect on the hyperfine frequency shift of an alkali metal vapor cell with paraffin coating using the peak-force tapping AFM (atomic force microscopy) technique by developing a uniform and high-quality paraffin coating method. We observed a relatively uniform temperature field on the substrate can be obtained theoretically and experimentally with the closed-type previse temperature-controlled evaporation method. The roughness and adhesion of the coating surface as low as 0.8 nm and 20 pN were successfully obtained, respectively. Furthermore, the adhesion information dependence of the topography was investigated from the force spectroscopy, which indicates that the adhesion force jumped on the edge of the particles and stepped but remained constant above the particles and steps regardless of their height for paraffin coating. Finally, we can evaluate the relaxation and the hyperfine frequency shift of an alkali metal vapor cell through accurately calculating the surface adsorption energy of the paraffin coating from peak-force tapping information. This finding is crucial for improving the sensitivity of the atomic sensors through directly analyzing the adhesion effect of the paraffin coating films instead of measuring the relaxation times.

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Comparison of Compensation Mechanism Between an NMR Gyroscope and an SERF Gyroscope

Cited by 9 publications

References 17 publications

Markerless motion capture for 3D human model animation using depth camera

Markerless motion capture for 3D human model animation using depth camera

Multimodal sensing for assisted living using radar

Adhesion Effect on the Hyperfine Frequency Shift of an Alkali Metal Vapor Cell with Paraffin Coating Using Peak-Force Tapping AFM

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