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Статті в журналах з теми "MCAE Systems"

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Автор: Grafiati

Опубліковано: 13 вересня 2021

Оновлено: 5 лютого 2022

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1

Gossard, D. C., R. P. Zuffante, and H. Sakurai. "Representing dimensions, tolerances, and features in MCAE systems." IEEE Computer Graphics and Applications 8, no. 2 (March 1988): 51–59. http://dx.doi.org/10.1109/38.503.

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2

Gossard, D. C., R. P. Zuffante, and H. Sakurai. "Representing dimensions, tolerances, and features in MCAE systems." Computer-Aided Design 20, no. 7 (September 1988): 423. http://dx.doi.org/10.1016/0010-4485(88)90228-x.

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3

Luo, Jiabin, Wentai Lei, Feifei Hou, Chenghao Wang, Qiang Ren, Shuo Zhang, Shiguang Luo, Yiwei Wang, and Long Xu. "GPR B-Scan Image Denoising via Multi-Scale Convolutional Autoencoder with Data Augmentation." Electronics 10, no. 11 (May 26, 2021): 1269. http://dx.doi.org/10.3390/electronics10111269.

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Ground-penetrating radar (GPR), as a non-invasive instrument, has been widely used in civil engineering. In GPR B-scan images, there may exist random noise due to the influence of the environment and equipment hardware, which complicates the interpretability of the useful information. Many methods have been proposed to eliminate or suppress the random noise. However, the existing methods have an unsatisfactory denoising effect when the image is severely contaminated by random noise. This paper proposes a multi-scale convolutional autoencoder (MCAE) to denoise GPR data. At the same time, to solve the problem of training dataset insufficiency, we designed the data augmentation strategy, Wasserstein generative adversarial network (WGAN), to increase the training dataset of MCAE. Experimental results conducted on both simulated, generated, and field datasets demonstrated that the proposed scheme has promising performance for image denoising. In terms of three indexes: the peak signal-to-noise ratio (PSNR), the time cost, and the structural similarity index (SSIM), the proposed scheme can achieve better performance of random noise suppression compared with the state-of-the-art competing methods (e.g., CAE, BM3D, WNNM).

4

Demidchik, Vadim, and Sergey Shabala. "Mechanisms of cytosolic calcium elevation in plants: the role of ion channels, calcium extrusion systems and NADPH oxidase-mediated 'ROS-Ca2+ Hub'." Functional Plant Biology 45, no. 2 (2018): 9. http://dx.doi.org/10.1071/fp16420.

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Elevation in the cytosolic free calcium is crucial for plant growth, development and adaptation. Calcium influx into plant cells is mediated by Ca2+ depolarisation-activated, hyperpolarisation-activated and voltage-independent Ca2+-permeable channels (DACCs, HACCs and VICCs respectively). These channels are encoded by the following gene families: (1) cyclic nucleotide-gated channels (CNGCs), (2) ionotropic glutamate receptors (GLRs), (3) annexins, (4) ‘mechanosensitive channels of small (MscS) conductance’-like channels (MSLs), (5) ‘mid1-complementing activity’ channels (MCAs), Piezo channels, and hyperosmolality-induced [Ca2+]cyt. channel 1 (OSCA1). Also, a ‘tandem-pore channel1’ (TPC1) catalyses Ca2+ efflux from the vacuole in response to the plasma membrane-mediated Ca2+ elevation. Recent experimental data demonstrated that Arabidopsis thaliana (L.) Heynh. CNGCs 2, 5–10, 14, 16 and 18, GLRs 1.2, 3.3, 3.4, 3.6 and 3.7, TPC1, ANNEXIN1, MSL9 and MSL10,MCA1 and MCA2, OSCA1, and some their homologues counterparts in other species, are responsible for Ca2+ currents and/or cytosolic Ca2+ elevation. Extrusion of Ca2+ from the cytosol is mediated by Ca2+-ATPases and Ca2+/H+ exchangers which were recently examined at the level of high resolution crystal structure. Calcium-activated NADPH oxidases and reactive oxygen species (ROS)-activated Ca2+ conductances form a self-amplifying ‘ROS-Ca2+hub’, enhancing and transducing Ca2+ and redox signals. The ROS-Ca2+ hub contributes to physiological reactions controlled by ROS and Ca2+, demonstrating synergism and unity of Ca2+ and ROS signalling mechanisms.

5

Fourlas, G. K., K. J. Kyriakopoulos, and C. D. Vournas. "Hybrid systems modeling for power systems." IEEE Circuits and Systems Magazine 4, no. 3 (2004): 16–23. http://dx.doi.org/10.1109/mcas.2004.1337806.

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6

MacLeod, C. L., K. D. Finley, and D. K. Kakuda. "y(+)-type cationic amino acid transport: expression and regulation of the mCAT genes." Journal of Experimental Biology 196, no. 1 (November 1, 1994): 109–21. http://dx.doi.org/10.1242/jeb.196.1.109.

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The transport of cationic amino acids across animal cell membranes is largely mediated by a small group of well-described transport system (y+, bo,+, Bo,+). Only recently have genes encoding transport proteins in some of these systems been isolated. Two genes, mCAT-1 and mCAT-2, encode related multiple membrane-spanning proteins that share substantial amino acid sequence identity and virtually superimposable hydrophilicity profiles. mCAT-1 and mCAT-2 proteins expressed in Xenopus oocytes are functionally indistinguishable and similar to transport system y+, but have distinct tissue distribution patterns. mCAT-1 expression is nearly ubiquitous and produces a single protein, while mCAT-2 is highly tissue-specific, has two distinct protein isoforms encoded by a single gene and is expressed in different tissues using at least two widely separated promoters. All three proteins facilitate the ion-independent transport of arginine, lysine and ornithine. Both mCAT-1 and mCAT-2 proteins have low amino acid sequence similarity but strikingly similar hydrophilicity profiles with amino acid antiporters, uniporters and symporters of yeast, fungi and eubacteria. Current work will elucidate whether any of the mCAT proteins interact with members of a newly identified family of single membrane-spanning proteins, such as rBAT, 4F2 and NAA-Tr, which are thought to modulate or activate y+L and/or bo,+ transport systems.

7

Chen, Jim X. "Geographic Information Systems." Computing in Science & Engineering 12, no. 1 (January 2010): 8–9. http://dx.doi.org/10.1109/mcse.2010.13.

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8

Putra, Alfyananda Kurnia, Muhammad Naufal Islam, Dian Ahmad Sasmito, and Alfa Yusrotin. "Implementasi m-learning berbasis Mobile Context Aware System (MCAS) dalam pembelajaran Geografi pada masa pandemi Covid-19." Jurnal Integrasi dan Harmoni Inovatif Ilmu-Ilmu Sosial 1, no. 5 (May 31, 2021): 591–97. http://dx.doi.org/10.17977/um063v1i5p591-597.

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Анотація:

Learning during the Covid-19 pandemic caused learning activity to be online and causes student’s boredom in Geography. Therefore, teachers must integrating the technology in learning process, with mobile learning (M-Learning) based on mobile context aware systems (MCAS). The study purpose is to determine student’s opinions about implementation of MCAS based M-Learning during the pandemic. This research is a descriptive qualitative with a mix method approach used collection techniques field research and literature study. The results showed that students had a positive opinion regarding the implementation of MCAS based M-Learning during the pandemic, with an average score of 3.40-3.70 out of 4.00. Pembelajaran pada masa pandemi Covid-19 menyebabkan pembelajaran menjadi online sehingga menyebabkan terjadinya kejenuhan siswa dalam proses pembelajaran Geografi. Oleh karena itu, guru harus mampu mengintegrasikan teknologi dalam pembelajaran, melalui mobile learning (M-Learning) berbasis mobile context aware systems (MCAS). Penelitian ini bertujuan mengetahui opini siswa dalam penerapan M-Learning berbasis MCAS pada masa pandemi. Jenis penelitian ini termasuk kualitatif deskriptif dengan pendekatan mix method serta teknik pengumpulan data berupa penelitian lapangan serta studi kepustakaan. Hasil penelitian menunjukkan bahwa siswa memiliki opini positif terkait implementasi M-Learning berbasis MCAS pada masa pandemi, dengan perolehan skor rata-rata skor 3,40-3,70 dari 4,00.

9

Sapuppo, Francesca, Florinda Schembri, Luigi Fortuna, and Maide Bucolo. "Microfluidic circuits and systems." IEEE Circuits and Systems Magazine 9, no. 3 (2009): 6–19. http://dx.doi.org/10.1109/mcas.2009.933853.

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10

An, Yunxia, Nan Wei, Xiangsong Cheng, Ying Li, Haiyang Liu, Jia Wang, Zhiwei Xu, Zhifu Sun, and Xiaoju Zhang. "MCAM abnormal expression and clinical outcome associations are highly cancer dependent as revealed through pan-cancer analysis." Briefings in Bioinformatics 21, no. 2 (February 28, 2019): 709–18. http://dx.doi.org/10.1093/bib/bbz019.

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Abstract MCAM (CD146) is a cell surface adhesion molecule that has been reported to promote cancer development, progression and metastasis and is considered as a potential tumor biomarker and therapeutic target. However, inconsistent reports exist, and its clinical value is yet to be confirmed. Here we took advantage of several large genomic data collections (Genotype-Tissue Expression, The Cancer Genome Atlas and Cancer Cell Line Encyclopedia) and comprehensively analyzed MCAM expression in thousands of normal and cancer samples and cell lines along with their clinical phenotypes and drug response information. Our results show that MCAM is very highly expressed in large vessel tissues while majority of tissues have low or minimal expression. Its expression is dramatically increased in a few tumors but significantly decreased in most other tumors relative to their pairing normal tissues. Increased MCAM expression is associated with a higher tumor stage and worse patient survival for some less common tumors but not for major ones. Higher MCAM expression in primary tumors may be complicated by tumor-associated or normal stromal blood vessels yet its significance may differ from the one from cancer cells. MCAM expression is weakly associated with the response to a few small molecular drugs and the association with targeted anti-BRAF agents suggests its involvement in that pathway which warrants further investigation.

11

Ortigueira, Manuel. "An introduction to the fractional continuous-time linear systems: the 21st century systems." IEEE Circuits and Systems Magazine 8, no. 3 (2008): 19–26. http://dx.doi.org/10.1109/mcas.2008.928419.

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12

Rigaut, François, and Benoit Neichel. "Multiconjugate Adaptive Optics for Astronomy." Annual Review of Astronomy and Astrophysics 56, no. 1 (September 14, 2018): 277–314. http://dx.doi.org/10.1146/annurev-astro-091916-055320.

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Since the year 2000, adaptive optics (AO) has seen the emergence of a variety of new concepts addressing particular science needs; multiconjugate adaptive optics (MCAO) is one of them. By correcting the atmospheric turbulence in 3D using several wavefront sensors and a tomographic phase reconstruction approach, MCAO aims to provide uniform diffraction limited images in the near-infrared over fields of view larger than 1 arcmin2, i.e., 10 to 20 times larger in area than classical single conjugated AO. In this review, we give a brief reminder of the AO principles and limitations, and then focus on aspects particular to MCAO, such as tomography and specific MCAO error sources. We present examples and results from past or current systems: MAD (Multiconjugate Adaptive Optics Demonstrator) and GeMS (Gemini MCAO System) for nighttime astronomy and the AO system, at Big Bear for solar astronomy. We examine MCAO performance (Strehl ratio up to 40% in H band and full width at half maximum down to 52 mas in the case of MCAO), with a particular focus on photometric and astrometric accuracy, and conclude with considerations on the future of MCAO in the Extremely Large Telescope and post–HST era.

13

Hasler, M. "Advances in circuits and systems." IEEE Circuits and Systems Magazine 5, no. 3 (2005): 30–31. http://dx.doi.org/10.1109/mcas.2005.1507523.

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14

Hasler, M. "Advances in circuits and systems." IEEE Circuits and Systems Magazine 6, no. 2 (2006): 49–50. http://dx.doi.org/10.1109/mcas.2006.1648990.

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15

Myers, Christopher R., Ryan N. Gutenkunst, and James P. Sethna. "Python Unleashed on Systems Biology." Computing in Science & Engineering 9, no. 3 (2007): 34–37. http://dx.doi.org/10.1109/mcse.2007.60.

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16

Gropp, William D. "Software for Petascale Computing Systems." Computing in Science & Engineering 11, no. 5 (September 2009): 17–21. http://dx.doi.org/10.1109/mcse.2009.148.

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17

Hinsen, Konrad, Konstantin Läufer, and George K. Thiruvathukal. "Essential Tools: Version Control Systems." Computing in Science & Engineering 11, no. 6 (November 2009): 84–91. http://dx.doi.org/10.1109/mcse.2009.194.

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18

Jensen, David, and Arun Rodrigues. "Embedded Systems and Exascale Computing." Computing in Science & Engineering 12, no. 6 (November 2010): 20–29. http://dx.doi.org/10.1109/mcse.2010.95.

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19

Oliver, Hilary, Matthew Shin, David Matthews, Oliver Sanders, Sadie Bartholomew, Andrew Clark, Ben Fitzpatrick, Ronald van Haren, Rolf Hut, and Niels Drost. "Workflow Automation for Cycling Systems." Computing in Science & Engineering 21, no. 4 (July 1, 2019): 7–21. http://dx.doi.org/10.1109/mcse.2019.2906593.

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20

Shi, Biaofei, Lu Xiong, and Zhuoping Yu. "Pressure Estimation of the Electro-Hydraulic Brake System Based on Signal Fusion." Actuators 10, no. 9 (September 16, 2021): 240. http://dx.doi.org/10.3390/act10090240.

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At present, the master cylinder pressure estimation algorithm (MCPE) of electro-hydraulic brake systems (EHB) based on vehicle dynamics has the disadvantages of poor condition adaptability, and there are delays and noise in the estimated pressure; however, the MCPE based on the characteristics of an EHB (i.e., the pressure–position relationship) is not robust enough to prevent brake pad wear. For the above reasons, neither method be applied to engineering. In this regard, this article proposes a MCPE that is based on signal fusion. First, a five-degree-of-freedom (5-DOF) vehicle model that includes longitudinal motion, lateral motion, yaw motion, and front and rear wheel rotation is established. Based on this, an algebraic expression for MCPE is derived, which extends the MCPE from a straight condition to a steering condition. Real vehicle tests show that the MCPE based on the 5-DOF vehicle model can effectively estimate the brake pressure in both straight and steering conditions. Second, the relationship between the hydraulic pressure and the rack position in the EHB is tested under different brake pad wear levels, and the results show that the pressure–position relationship will change as the brake pad is worn down, so the pressure estimated by the pressure–position model based on fixed parameters is not robust. Third, a MCPE based on the fusion the above two MCPEs through the recursive least squares algorithm (RLS) is proposed, in which the pressure-position model can be updated online by vehicle dynamics and the final estimated pressure is calculated based on the updated pressure–position model. Finally, several simulations based on vehicle test data demonstrate that the fusion-based MCPE can estimate the brake pressure accurately and smoothly with little delay and is robust enough to prevent brake pad wear. In addition, by setting the enabling conditions of RLS, the fusion-based MCPE can switch between driving and parking smoothly; thus, the fusion-based MCPE can be applied to all working conditions.

21

Zamora, I., A. J. Mazon, K. J. Sagastabeitia, O. Pico, and J. R. Saenz. "Verifying resonant grounding in distribution systems." IEEE Computer Applications in Power 15, no. 4 (October 2002): 45–50. http://dx.doi.org/10.1109/mcap.2002.1046111.

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22

Fodor, G. "Signals, systems and networks (Book Reviews)." IEEE Circuits and Systems Magazine 3, no. 2 (2003): 24–25. http://dx.doi.org/10.1109/mcas.2003.1242833.

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23

Ogorzalek, M. J. "Dear Circuits and Systems Society Members." IEEE Circuits and Systems Magazine 4, no. 1 (2004): 3. http://dx.doi.org/10.1109/mcas.2004.1286977.

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24

Polikar, R. "Ensemble based systems in decision making." IEEE Circuits and Systems Magazine 6, no. 3 (2006): 21–45. http://dx.doi.org/10.1109/mcas.2006.1688199.

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25

Kwasinski, Andres. "Signals, Systems, and Design [Book review]." Computing in Science & Engineering 19, no. 4 (2017): 72–73. http://dx.doi.org/10.1109/mcse.2017.3151251.

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26

Turilli, Matteo, Vivek Balasubramanian, Andre Merzky, Ioannis Paraskevakos, and Shantenu Jha. "Middleware Building Blocks for Workflow Systems." Computing in Science & Engineering 21, no. 4 (July 1, 2019): 62–75. http://dx.doi.org/10.1109/mcse.2019.2920048.

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27

Bertino, E., E. Ferrari, and A. Squicciarini. "Trust negotiations: concepts, systems, and languages." Computing in Science and Engineering 6, no. 4 (July 2004): 27–34. http://dx.doi.org/10.1109/mcse.2004.22.

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28

Shi, Biaofei, Lu Xiong, and Zhuoping Yu. "Pressure Estimation Based on Vehicle Dynamics Considering the Evolution of the Brake Linings’ Coefficient of Friction." Actuators 10, no. 4 (April 8, 2021): 76. http://dx.doi.org/10.3390/act10040076.

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Анотація:

To mitigate the issue of low accuracy and poor robustness of the master cylinder pressure estimation (MCPE) of the electro-hydraulic brake system (EHB) by adopting EHB’s own information, a MCPE algorithm based on vehicle information considering the evolution of the brake linings’ coefficient of friction (BLCF) is proposed. First, the MCPE algorithm was derived combining the vehicle longitudinal dynamics and the wheel dynamics, in which the inertial measurement unit (IMU) was adopted to adapt the MCPE algorithm to road slope change. In order to estimate the brake pressure accurately, the driving resistance of the vehicle was obtained through a vehicle test under coasting condition. After that, with the active braking function of EHB, the evolution of the BLCF was acquired through extensive real vehicle test under different initial temperatures, different initial vehicle speeds, and different brake pressures. According to the test results, a revised model of the BLCF is proposed. Finally, the performance of the MCPE based on the revised BLCF model was compared with that based on a fixed BLCF model. Vehicle test demonstrates that the former MCPE algorithm is not only more accurate at low vehicle speed than the later, but also robust to road slope change.

29

Chowdhury, S., M. Ahmadi, and W. C. Miller. "Microelectromechanical systems and system-on-chip connectivity." IEEE Circuits and Systems Magazine 2, no. 2 (2002): 4–28. http://dx.doi.org/10.1109/mcas.2002.1045855.

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30

Kocarev, L. "Chaos in circuits and systems [Book Review]." IEEE Circuits and Systems Magazine 3, no. 1 (2003): 22–23. http://dx.doi.org/10.1109/mcas.2003.1228504.

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31

Jin Liu and Xiaofeng Lin. "Feature - Equalization in high-speed communication systems." IEEE Circuits and Systems Magazine 4, no. 2 (2004): 4–17. http://dx.doi.org/10.1109/mcas.2004.1330746.

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32

Jie Chen, E. Dougherty, S. S. Demir, C. Friedman, Chung Sheng Li, and S. Wong. "Grand challenges for multimodal bio-medical systems." IEEE Circuits and Systems Magazine 5, no. 2 (2005): 46–52. http://dx.doi.org/10.1109/mcas.2005.1438739.

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33

Garulli, A., and A. Vicino. "Convex relaxations in circuits, systems, and control." IEEE Circuits and Systems Magazine 9, no. 2 (2009): 46–56. http://dx.doi.org/10.1109/mcas.2008.931737.

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34

Yelten, M. B., Ting Zhu, S. Koziel, P. D. Franzon, and M. B. Steer. "Demystifying Surrogate Modeling for Circuits and Systems." IEEE Circuits and Systems Magazine 12, no. 1 (2012): 45–63. http://dx.doi.org/10.1109/mcas.2011.2181095.

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35

Knipp, Peter A., and S. Raj Chaudhury. "Postprocessing in Automated Grading Systems, Part 2." Computing in Science & Engineering 11, no. 4 (July 2009): 82–85. http://dx.doi.org/10.1109/mcse.2009.125.

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36

Knipp, Peter A., and S. Raj Chaudhury. "Postprocessing in Automated Grading Systems, Part 1." Computing in Science & Engineering 11, no. 3 (May 2009): 64–67. http://dx.doi.org/10.1109/mcse.2009.58.

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37

Sousa, Leonel. "Nonconventional Computer Arithmetic Circuits, Systems and Applications." IEEE Circuits and Systems Magazine 21, no. 1 (2021): 6–40. http://dx.doi.org/10.1109/mcas.2020.3027425.

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38

Romantowski, Jan, Aleksandra Górska, Marek Niedoszytko, Theo Gulen, Marta Gruchała-Niedoszytko, Bogusław Nedoszytko, Magdalena Lange, et al. "A Challenge for Allergologist: Application of Allergy Diagnostic Methods in Mast Cell Disorders." International Journal of Molecular Sciences 22, no. 3 (February 1, 2021): 1454. http://dx.doi.org/10.3390/ijms22031454.

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Анотація:

Primary and secondary mast cell activation syndromes (MCAS) can occur in patients with mastocytosis. During the past few years our knowledge about the pathogenesis and disease-triggering mechanisms in MCAS and mastocytosis have increased substantially. Whereas mastocytosis is characterized by an accumulation of neoplastic (clonal) mast cells (MC) in various organ systems, MCAS is defined by a massive and systemic activation of these cells. Mast cells are crucial effector cells in allergic diseases, thus their elevated number and activation can cause severe anaphylactic reactions and MCAS in patients with mastocytosis. However, these cells may also degranulate spontaneously or degranulate in response to non-allergic triggers leading to clinical symptoms. In mastocytosis patients, such symptoms may lead to the diagnosis of a primary MCAS. The diagnosis of a concomitant allergy in mastocytosis patients is challenging. In these patients, a mixed form (primary and secondary) of MCAS may be diagnosed. These patients may also suffer from life-threatening anaphylactic reactions when exposed to allergens. In these cases, the possibility of severe side effects of in vivo provocations can sometimes also limit diagnostic evaluations. In the current article, we discuss the diagnosis and management of patients suffering from mastocytosis and concomitant MCAS, with special emphasis on novel diagnostic tests and management, including allergen microarrays, recombinant allergen analysis, basophil activation tests, optimal prophylaxis, and specific therapies.

39

Akhoundzadeh, Kobra, and Abedin Vakili. "Occurrence of priapism after transient right MCAO in Swiss albino mice." Somatosensory & Motor Research 36, no. 2 (April 3, 2019): 151–55. http://dx.doi.org/10.1080/08990220.2019.1632182.

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40

Kak, S. "Artificial Intelligence Techniques In Power Systems [Book Review]." IEEE Computer Applications in Power 11, no. 1 (January 1998): 71. http://dx.doi.org/10.1109/mcap.1998.648536.

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41

Amelink, H. "Power systems engineers in the restructured utility industry." IEEE Computer Applications in Power 14, no. 1 (January 2001): 10–12. http://dx.doi.org/10.1109/mcap.2001.893349.

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42

Nardini, C., L. Benini, and G. De Micheli. "Feature - Circuits and systems for high-throughput biology." IEEE Circuits and Systems Magazine 6, no. 3 (2006): 10–20. http://dx.doi.org/10.1109/mcas.2006.1688198.

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Wang, Xiaofan, Xiang Li, and Jinhu Lu. "Control and Flocking of Networked Systems via Pinning." IEEE Circuits and Systems Magazine 10, no. 3 (2010): 83–91. http://dx.doi.org/10.1109/mcas.2010.937887.

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