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Number of results: 8
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Abstract

Efektywność posłużenia się analogią w nauce zależy od stopnia adekwatności danej analogii. Teza ta jest poddana sprawdzeniu w kontekście analogii, zarówno biologicznych, jak i informatycznych użytych w teorii memów kulturowych, jako podstawy ewolucyjnego rozwoju nauki, czy szerzej kultury. Uwidoczniony w pracy problem z wyróżnieniem kulturowego odpowiednika biologicznego osobnika ma wpływ na rodzaj ewolucji – darwinowski czy lamarckowski.
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Authors and Affiliations

Marek Suwara
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Abstract

The article is a brief presentation of the relationship between the politics of memory and Facebook. This type of connection advantages aestheticism, pictures and emotional infl uence but discounts traditional instruments modelling collective memory. The article focuses on the answer to the question of how a popular culture aesthetic infi ltrates and changes the politics of memory.

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Authors and Affiliations

Łukasz Włodarczyk
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Abstract

Many groups of researchers have focused on the design of micro turbine engines in recent years. Since turbo-component efficiency becomes very low due to the downsizing effect, an important problem arises of how to obtain thermal efficiency high enough to produce the positive power required. The micro wave rotor is expected to be applied for the improvement of the performance of ultra micro gas turbines, increasing the cycle pressure ratio. Wave rotors can also be built in another configuration. Applying only a combustion chamber and using oblique blades to form the rotor cells, net power can be taken from the rotor. In that way, the use in a micro scale of an inefficient turbo unit can be omitted. Such a solution in a form of wave engine was developed and practically realised by Weber [ 15] and Pearson [8], [9], [ IO] in centimetre scale. Conventional construction of wave engines in a form of wave rotor can not be directly realized in MEMS technology. The new idea of a wave disk developed by Piechna, Akbari, Iancu,and Mueller [II] and independently by Nagashima and Okamoto [7] gives the possibility of easy implementation of the wave engine idea in MEMS technology. In the proposed solution, the wave disk plays the role of an active compressiondecompression unit and torque generator. Appropriate port geometry with oblique blades forming the disk channels generates torque. The engine disk rotates with a speed much lower than the conventional turbo-unit that simplifies the bearing problem. Also, the construction of electric generator can be simpler. The paper presents the proposed flow schemes, thermodynamic cycle, exemplary engine construction and some results of simulation of the MEMS wave engine using the wave disk.
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Authors and Affiliations

Janusz R. Piechna
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Abstract

The article presents the possibilities of using popular MEMS inertial sensors in the object tilt angle estimation system and in the system for stabilizing the vertical position of the balancing robot. Two research models were built to conduct the experiment. The models use microcontroller development board of the STM32F3 series with the Cortex-M4 core, equipped with a three-axis accelerometer, magnetometer and gyroscope. To determine the accuracy of the angle estimation, comparative tests with a pulse encoder were performed.
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Bibliography

[1] P. Groves, “Principles of GNSS, Inertial, and Multisensor Integrated Navigation Systems”, Norwood, MA: Artech House, 2008.
[2] J. Collin, P. Davidson, M. Kirkko-Jaakkola, H. Leppäkoski “Inertial Sensors and Their Applications” S. Bhattacharyya, E. Deprettere, R. Leupers, J.Takala “Handbook of Signal Processing Systems”. Springer, 2019, pp.51-85. DOI 10.1007/978-3-319-91734-4_2
[3] M. Labowski, P. Kaniewski, P. Serafin, "Inertial Navigation System for Radar Terrain Imaging," Proceedings of IEEE/ION PLANS 2016, Savannah, GA, pp. 942-948, April 2016.
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[6] J. M. Darmanin et al., "Development of a High-G Shock Sensor Based on MEMS Technology for Mass-Market Applications," 2019 IEEE International Symposium on Inertial Sensors and Systems (INERTIAL), Naples, FL, USA, 2019, pp. 1-4, DOI: 10.1109/ISISS.2019.8739763.
[7] M. Mansoor, I. Haneef, S. Akhtar, M. A. Rafiq, S. Z. Ali and F. Udrea, "SOI CMOS multi-sensors MEMS chip for aerospace applications," SENSORS, 2014 IEEE, Valencia, Spain, 2014, pp. 1204-1207, DOI: 10.1109/ICSENS.2014.6985225.
[8] A. Mikov, A. Panyov, V. Kosyanchuk and I. Prikhodko, "Sensor Fusion For Land Vehicle Localization Using Inertial MEMS and Odometry," 2019 IEEE International Symposium on Inertial Sensors and Systems (INERTIAL), Naples, FL, USA, 2019, pp. 1-2, DOI: 10.1109/ISISS.2019.8739427.
[9] C. Acar, "High-performance 6-Axis MEMS inertial sensor based on Through-Silicon Via technology," 2016 IEEE International Symposium on Inertial Sensors and Systems, Laguna Beach, CA, 2016, pp. 62-65, DOI: 10.1109/ISISS.2016.7435545.
[10] I. P. Prikhodko, B. Bearss, C. Merritt, J. Bergeron and C. Blackmer, "Towards self-navigating cars using MEMS IMU: Challenges and opportunities," 2018 IEEE International Symposium on Inertial Sensors and Systems (INERTIAL), Moltrasio, 2018, pp. 1-4, DOI: 10.1109/ISISS.2018.8358141.
[11] R. E. Kalman, "A New Approach to Linear Filtering and Prediction Problems." ASME. J. Basic Eng. March 1960; vol. 82, no1, pp. 35–45. DOI 10.1115/1.3662552
[12] J. Gajda, R. Sroka, M. Stencel, T. Żegleń, “Data fusion applications in the traffic parameters measurement”, Metrology and Measurement Systems, vol. 2, no. 3, pp. 249–262, 2005.
[13] S.Chudzik, “The idea of using artificial neural network in measurement system with hot probe for testing parameters of heat-insulating materials”, Measurement, vol. 42 pp. 764–770, 2009.
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Authors and Affiliations

Stanisław Chudzik
1

  1. Czestochowa University of Technology, Czestochowa, Poland
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Abstract

This paper proposes a new approach called the Predictive Kalman Filter (PKF) which predicts and compensates model errors of inertial sensors to improve the accuracy of static alignment without the use of external assistance. The uncertain model error is the main problem in the field as the Micro Electro Mechanical System (MEMS) inertial sensors have bias which change over time, and these errors are not all observable. The proposed filter determines an optimal equivalent model error by minimizing a quadratic penalty function without augmenting the system state space. The optimization procedure enables the filter to decrease both model uncertainty and external disturbances. The paper first presents the complete formulation of the proposed filter. Then, a nonlinear alignment model with a large misalignment angle is considered. Experimental results demonstrate that the new method improves the accuracy and rapidness of the alignment process as the convergence time is reduced from 550 s to 50 s, and the azimuth misalignment angle correctness is decreased from 52" 47" to 4" 0:02".
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Bibliography

[1] Britting, K. R. (1971). Inertial navigation systems analysis. Wiley Interscience.
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[3] Xue, H., Guo, X., & Zhou, Z. (2016). Parameter identification method for SINS initial alignment under inertial frame. Mathematical Problems in Engineering, 2016, 5301242. https://doi.org/10.1155/2016/5301242
[4] Wang, D., Dong, Y., Li, Q., Wu, J., & Wen, Y. (2018). Estimation of small UAV position and attitude with reliable in-flight initial alignment for MEMSinertial sensors. Metrology and Measurement Systems, 25(3), 603–616. https://doi.org/10.24425/123904
[5] Ghanbarpourasl, H. (2020). A new robust quaternion-based initial alignment algorithm for stationary strapdown inertial navigation systems. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 234(12), 1913–1925. https://doi.org/10.1177/0954410020920473
[6] Guo, S., Chang, L., Li, Y., & Sun, Y. (2020). Robust fading cubature Kalman filter and its application in initial alignment of SINS. Optik, 202, 163593. https://doi.org/10.1016/j.ijleo.2019.163593
[7] Zhang, T., Wang, J., Jin, B., & Li, Y. (2019). Application of improved fifth-degree cubature Kalman filter in the nonlinear initial alignment of strapdown inertial navigation system. Review of Scientific Instruments, 90(1), 015111. https://doi.org/10.1063/1.5061790
[8] Xing, H., Chen, Z.,Wang, C., Guo, M., & Zhang, R. (2019). Quaternion-based Complementary Filter for Aiding in the Self-Alignment of the MEMS IMU. 2019 IEEE International Symposium on Inertial Sensors and Systems (INERTIAL), USA, 1–4. https://doi.org/10.1109/ISISS.2019.8739728
[9] Yang, B., Xu, X., Zhang, T., Sun, J., & Liu, X. (2017). Novel SINS initial alignment method under large misalignment angles and uncertain noise based on nonlinear filter. Mathematical Problems in Engineering, 2017, 5917917. https://doi.org/10.1155/2017/5917917
[10] Sun, J., Xu, X., Liu, Y., Zhang, T., & Li, Y. (2015). Initial alignment of large azimuth misalignment angles in SINS based on adaptive UPF. Sensors, 15(9), 21807–21823. https://doi.org/10.3390/s150921807
[11] Han, H., Wang, J., & Du, M. (2017). A fast SINS initial alignment method based on RTS forward and backward resolution. Journal of Sensors, 2017, 7161858. https://doi.org/10.1155/2017/7161858
[12] Kaygısız, B. H., & Sen, B. (2015). In-motion alignment of a low-cost GPS/INS under large heading error. The Journal of Navigation, 68(2), 355–366. https://doi.org/10.1017/S0373463314000629
[13] Xia, X.,&Sun, Q. (2018). Initial alignment algorithm based on theDMCSmethod in single-axis RSINS with large azimuth misalignment angles for submarines. Sensors, 18(7), 1807–2123. https://doi.org/10.3390/s18072123
[14] Li, J., Gao, W., Zhang, Y., & Wang, Z. (2018). Gradient Descent Optimization-Based Self-Alignment Method for Stationary SINS. IEEE Transactions on Instrumentation and Measurement, 68(9), 3278– 3286. https://doi.org/10.1109/TIM.2018.2878071
[15] Camacho, E. F., Ramírez, D. R., Limón, D., De La Peña, D. M., & Alamo, T. (2010). Model predictive control techniques for hybrid systems. Annual Reviews in Control, 34(1), 21–31. https://doi.org/10.1016/j.arcontrol.2010.02.002
[16] Titterton, D., Weston, J. L., & Weston, J. (2004). Strapdown inertial navigation technology. IET. https://doi.org/10.1049/PBRA017E
[17] Analog Devices. (2018). Tactical Grade Ten Degrees of Freedom Inertial Sensor – ADIS16488A. [Datasheet, Rev. F]. https://www.analog.com/media/en/technical-documentation/data-sheets/ADIS16488A.pdf
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Authors and Affiliations

Hassan Majed Alhassan
1
Nemat Allah Ghahremani
1

  1. Malek Ashtar University of Technology, Faculty of Electrical & Computer Engineering, Tehran 15875-1774, Iran
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Abstract

Eye tracking systems are mostly video-based methods which require significant computation to achieve good accuracy. An alternative method with comparable accuracy but less computational expense is 2D microelectromechanical (MEMS) mirror scanning. However, this technology is relatively new and there are not many publications on it. The purpose of this study was to examine how individual parameters of system components can affect the accuracy of pupil position estimation. The study was conducted based on a virtual simulator. It was shown that the optimal detector field of view (FOV) depends on the frequency ratio of the MEMS mirror axis. For a value of 1:13, the smallest errors were at 0.°, 1.65°, 2.3°, and 2.95°. The error for the impact of the signal sampling rate above 3 kHz stabilizes at 0.065° and no longer changes its value regardless of increasing the number of samples. The error for the frequency ratio of the MEMS mirror axis increases linearly in the range of 0.065°–0.1°up to the ratio of 1:230. Above this there is a sudden increase to the average value of 0.3°. The conducted research provides guidance in the selection of parameters for the construction of eye tracking MEMS mirror-based systems.
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Bibliography

[1] Duchowski, A. T., (2017). Eye tracking methodology: Theory and practice. Springer. https://doi.org/10.1007/978-3-319-57883-5
[2] Judd, T., Ehinger, K., Durand, F., & Torralba, A. (2009, September). Learning to predict where humans look. IEEE 12th International Conference on Computer Vision (pp. 2106–2113). IEEE. https://doi.org/10.1109/ICCV.2009.5459462
[3] Goldberg, J. H., & Kotval, X. P. (1999). Computer interface evaluation using eye movements: methods and constructs. International Journal of Industrial Ergonomics, 24(6), 631–645. https://doi.org/10.1016/S0169-8141(98)00068-7
[4] Hansen, D. W., & Ji, Q. (2009). In the eye of the beholder: A survey of models for eyes and gaze. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(3), 478–500. https://doi.org/10.1109/TPAMI.2009.30
[5] Carvalho, N., Laurent, E., Noiret, N., Chopard, G., Haffen, E., Bennabi, D., & Vandel, P. (2015). Eye movement in unipolar and bipolar depression: A systematic review of the literature. Frontiers in Psychology, 6, 1809. https://doi.org/10.3389/fpsyg.2015.01809
[6] Bittencourt, J., Velasques, B., Teixeira, S., Basile, L. F., Salles, J. I., Nardi, A. E., Budde, H., Cagy, M., Piedade, R., & Ribeiro, P. (2013). Saccadic eye movement applications for psychiatric disorders. Neuropsychiatric Disease and Treatment, 9, 1393. https://doi.org/10.2147/NDT.S45931
[7] Duchowski, A. T., Medlin, E., Gramopadhye, A., Melloy, B., & Nair, S. (2001, November). Binocular eye tracking in VR for visual inspection training. Proceedings of the ACM symposium on Virtual reality software and technology (pp. 1–8). https://doi.org/10.1145/505008.505010
[8] Blattgerste, J., Renner, P., & Pfeiffer, T. (2018, June). Advantages of eye-gaze over head-gaze-based selection in virtual and augmented reality under varying field of views. Proceedings of the Workshop on Communication by Gaze Interaction (pp. 1–9). https://doi.org/10.1145/3206343.3206349
[9] Pasarica, A., Bozomitu, R. G., Cehan, V., Lupu, R. G., & Rotariu, C. (2015, October). Pupil detection algorithms for eye tracking applications. 2015 IEEE 21st International Symposium for Design and Technology in Electronic Packaging (SIITME) (pp. 161–164). IEEE. https://doi.org/10.1109/SIITME.2015.7342317 [10] Stengel, M., Grogorick, S., Eisemann, M., Eisemann, E., & Magnor, M. A. (2015, October). An affordable solution for binocular eye tracking and calibration in head-mounted displays. Proceedings of the 23rd ACM international conference on Multimedia (pp. 15–24). https://doi.org/10.1145/2733373.2806265
[11] Wen, Q., Bradley, D., Beeler, T., Park, S., Hilliges, O.,Yong, J.,&Xu, F. (2020).Accurate Real-time 3D Gaze Tracking Using a Lightweight Eyeball Calibration. Computer Graphics Forum, 39(2), 475–485. https://doi.org/10.1111/cgf.13945
[12] Lee, G. J., Jang, S. W., & Kim, G. Y. (2020). Pupil detection and gaze tracking using a deformable template. Multimedia Tools and Applications, 79(19), 12939–12958. https://doi.org/10.1007/ s11042-020-08638-7
[13] Gegenfurtner, A., Lehtinen, E., & Säljö, R. (2011). Expertise differences in the comprehension of visualizations: A meta-analysis of eye-tracking research in professional domains. Educational Psychology Review, 23(4), 523–552. https://doi.org/10.1007/s10648-011-9174-7
[14] Sarkar, N., O’Hanlon, B., Rohani, A., Strathearn, D., Lee, G., Olfat, M., & Mansour, R. R. (2017, January). A resonant eye-tracking microsystem for velocity estimation of saccades and foveated rendering. IEEE 30th International Conference on Micro Electro Mechanical Systems (MEMS) (pp. 304–307). IEEE. https://doi.org/10.1109/MEMSYS.2017.7863402
[15] Bartuzel, M. M., Wróbel, K., Tamborski, S., Meina, M., Nowakowski, M., Dalasinski, K., Szkulmowska, A. & Szkulmowski, M. (2020). High-resolution, ultrafast, wide-field retinal eye-tracking for enhanced quantification of fixational and saccadic motion. Biomedical Optics Express, 11(6), 3164–3180. https://doi.org/10.1364/BOE.392849
[16] Meyer, J., Schlebusch, T., Fuhl, W., & Kasneci, E. (2020). A novel camera-free eye tracking sensor for augmented reality based on laser scanning. IEEE Sensors Journal, 20(24), 15204–15212. https://doi.org/10.1109/JSEN.2020.3011985
[17] Pomianek, M., Piszczek, M., Maciejewski, M., & Krukowski, P. (2020, October). Pupil Position Estimation Error in an Eye Tracking System Based on the MEMS Mirror Scanning Method. Proceedings of the 3rd International Conference on Microelectronic Devices and Technologies (MicDAT’ 2020) (pp. 28–30). IFSA.
[18] Pengfei, Y., Zhengming, C., Jing, T., & Lina, Q. (2016). Virtual Simulation System of Cutter Suction Dredger Based on Unity3D. Journal of Systems Simulation, 28(9), 2069–2075.
[19] Richards, D., & Taylor, M. (2015). A Comparison of learning gains when using a 2D simulation tool versus a 3D virtual world: An experiment to find the right representation involving the Marginal Value Theorem. Computers & Education, 86, 157–171. https://doi.org/10.1016/j.compedu.2015.03.009
[20] Müller, L. M., Mandon, K., Gliesche, P., Weiß, S., & Heuten, W. (2020, November). Visualization of Eye Tracking Data in Unity3D. 19th International Conference on Mobile and Ubiquitous Multimedia (pp. 343–344). https://doi.org/10.1145/3428361.3431194
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Authors and Affiliations

Mateusz Pomianek
1
Marek Piszczek
1
Marcin Maciejewski
1

  1. Military University of Technology, Institute of Optoelectronics, 2 Kaliskiego St., 00-908 Warsaw, Poland
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Abstract

The MEMS inclinometer integrates a tri-axis accelerometer and a tri-axis gyroscope to solve the perceived dynamic inclinations through a complex data fusion algorithm, which has been widely used in the fields of industrial, aerospace, and monitoring. In order to ensure the validity of the measurement results of MEMS inclinometers, it is necessary to determine their dynamic performance parameters. This study proposes a conical motion-based MEMS inclinometer dynamic testing method, and the motion includes the classical conical motion, the attitude conical motion, and the dual-frequency conical motion. Both the frequency response and drift angle of MEMS inclinometers can be determined. Experimental results show that the conical motions can accelerate the angle drift of MEMS inclinometers, which makes them suitable for dynamic testing ofMEMSinclinometers. Additionally, the tilt sensitivity deviation of theMEMS inclinometer by the proposed method and the turntable-based method is less than 0.26 dB.We further provide the research for angle drift and provide discussion.
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Authors and Affiliations

Qihang Yang
1
Chenguang Cai
2
Ming Yang
3
Ming Kong
1
Zhihua Liu
2
Feng Liang
4

  1. College of Metrology and Measurement Engineering, China Jiliang University, Hangzhou 310018, China
  2. National Institute of Metrology of China, Beijing 100013, China
  3. College of Electrical Engineering, Guizhou University, Guiyang 550025, China
  4. Shenyang Aircraft Corporation, Shenyang 110031, China
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Abstract

Numerous technological applications use MEMS capacitive sensing technique as a major component, because of their ease of fabrication process, inexpensive and high sensitivity. The paper aims at modeling interdigitated capacitive (IDC) sensing. Virtually observe the contribution of variations in geometrical parameters to sensor efficiency and optimization factor. The sensor design is verified through ANSYS simulations. Results indicate “an efficient but poorly optimized sensor is better than a well-optimized sensor”. It is difficult to detect capacitance in the range of few pF generated using capacitive sensing. How it can be maximized with dimension optimization is focused in this paper.
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Authors and Affiliations

Vaishali Sanjay Kulkarni
1
Suvarna Sandip Chorage
2

  1. Department of E&TC at AISSMSIOIT-Pune,India
  2. Department of E&TC at BVCOEW-Pune, India

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