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Journal articles on the topic 'Time domain'

To see the other types of publications on this topic, follow the link: Time domain.

Author: Grafiati

Published: 4 June 2021

Last updated: 7 September 2024

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1

Krumpholz, M., H. G. Winful, and L. P. B. Katehi. "Nonlinear time-domain modeling by multiresolution time domain (MRTD)." IEEE Transactions on Microwave Theory and Techniques 45, no. 3 (March 1997): 385–93. http://dx.doi.org/10.1109/22.563337.

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2

Szyper, M. "Time domain window." Electronics Letters 31, no. 9 (1995): 707. http://dx.doi.org/10.1049/el:19950494.

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3

Nuss, Martin C., and Rick L. Morrison. "Time-domain images." Optics Letters 20, no. 7 (April 1, 1995): 740. http://dx.doi.org/10.1364/ol.20.000740.

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4

Djorgovski, S. G. "Time-Domain Astroinformatics." Proceedings of the International Astronomical Union 14, S339 (November 2017): 23. http://dx.doi.org/10.1017/s1743921318002144.

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AbstractTime-Domain astronomy exercises all aspects of the Virtual Observatory framework and Astroinformatics. Applications of machine learning and statistics to the analysis of large numbers of light-curves will increasingly yield new results as the data accumulate. However, the most challenging problems remain in the arena of rapid classification of transient events and their automated follow-up prioritisation. The talk illustrated those issues with examples from recent or ongoing synoptic sky surveys.

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5

Ferrari, C., G. Salvetti, E. Tognoni, and E. Tombari. "Time-domain and frequency-domain differential calorimetry." Journal of Thermal Analysis 47, no. 1 (July 1996): 75–85. http://dx.doi.org/10.1007/bf01982687.

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6

Chaber, S., H. Helbig, and MA Gamulescu. "Time-domain-OCT versus Frequency-domain-OCT." Der Ophthalmologe 107, no. 1 (June 6, 2009): 36–40. http://dx.doi.org/10.1007/s00347-009-1941-1.

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7

Yee, K. S., and J. S. Chen. "Conformal hybrid finite difference time domain and finite volume time domain." IEEE Transactions on Antennas and Propagation 42, no. 10 (1994): 1450–55. http://dx.doi.org/10.1109/8.320754.

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8

Brandt, Anders, and Rune Brincker. "Integrating time signals in frequency domain – Comparison with time domain integration." Measurement 58 (December 2014): 511–19. http://dx.doi.org/10.1016/j.measurement.2014.09.004.

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9

Peng, Furong, Jiachen Luo, Xuan Lu, Sheng Wang, and Feijiang Li. "Cross-Domain Contrastive Learning for Time Series Clustering." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 8 (March 24, 2024): 8921–29. http://dx.doi.org/10.1609/aaai.v38i8.28740.

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Most deep learning-based time series clustering models concentrate on data representation in a separate process from clustering. This leads to that clustering loss cannot guide feature extraction. Moreover, most methods solely analyze data from the temporal domain, disregarding the potential within the frequency domain. To address these challenges, we introduce a novel end-to-end Cross-Domain Contrastive learning model for time series Clustering (CDCC). Firstly, it integrates the clustering process and feature extraction using contrastive constraints at both cluster-level and instance-level. Secondly, the data is encoded simultaneously in both temporal and frequency domains, leveraging contrastive learning to enhance within-domain representation. Thirdly, cross-domain constraints are proposed to align the latent representations and category distribution across domains. With the above strategies, CDCC not only achieves end-to-end output but also effectively integrates frequency domains. Extensive experiments and visualization analysis are conducted on 40 time series datasets from UCR, demonstrating the superior performance of the proposed model.

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10

Harvey, Andrew, E. J. Hannan, P. R. Krishnaiah, and M. M. Rao. "Time Series in the Time Domain." Journal of the Royal Statistical Society. Series A (General) 149, no. 4 (1986): 404. http://dx.doi.org/10.2307/2981729.

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11

Yu, Guoyou, W. J. Mansur, J. A. M. Carrer, and L. Gong. "Time weighting in time domain BEM." Engineering Analysis with Boundary Elements 22, no. 3 (October 1998): 175–81. http://dx.doi.org/10.1016/s0955-7997(98)00042-3.

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12

Ghadiri, M., and R. Krechetnikov. "Pattern formation on time-dependent domains." Journal of Fluid Mechanics 880 (October 7, 2019): 136–79. http://dx.doi.org/10.1017/jfm.2019.659.

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In the quest to understand the dynamics of distributed systems on time-dependent spatial domains, we study experimentally the response to domain deformations by Faraday wave patterns – standing waves formed on the free surface of a liquid layer due to its vertical vibration – chosen as a paradigm owing to their historical use in testing new theories and ideas. In our experimental set-up of a vibrating water container with controlled positions of lateral walls and liquid layer depth, the characteristics of the patterns are measured using the Fourier transform profilometry technique, which allows us to reconstruct an accurate time history of the pattern three-dimensional landscape and reveal how it reacts to the domain dynamics on various length and time scales. Analysis of Faraday waves on growing, shrinking and oscillating domains leads to a number of intriguing results. First, the observation of a transverse instability – namely, when a two-dimensional pattern experiences an instability in the direction orthogonal to the direction of the domain deformation – provides a new facet to the stability picture compared to the one-dimensional systems in which the longitudinal (Eckhaus) instability accounts for pattern transformation on time-varying domains. Second, the domain evolution rate is found to be a key factor dictating the patterns observed on the path between the initial and final domain aspect ratios. Its effects range from allowing the formation of complex sequences of patterns to impeding the appearance of any new pattern on the path. Third, the shrinkage–growth process turns out to be generally irreversible on a horizontally evolving domain, but becomes reversible in the case of a time-dependent liquid layer depth, i.e. when the dilution and convective effects of the domain flow are absent. These experimentally observed enigmatic effects of the domain size variations in time are complemented here with appropriate theoretical insights elucidating the dynamics of two-dimensional pattern evolution, which proves to be more intricate compared to one-dimensional systems.

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13

Wu, Yanan, Zhixiang Chi, Yang Wang, Konstantinos N. Plataniotis, and Songhe Feng. "Test-Time Domain Adaptation by Learning Domain-Aware Batch Normalization." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 14 (March 24, 2024): 15961–69. http://dx.doi.org/10.1609/aaai.v38i14.29527.

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Test-time domain adaptation aims to adapt the model trained on source domains to unseen target domains using a few unlabeled images. Emerging research has shown that the label and domain information is separately embedded in the weight matrix and batch normalization (BN) layer. Previous works normally update the whole network naively without explicitly decoupling the knowledge between label and domain. As a result, it leads to knowledge interference and defective distribution adaptation. In this work, we propose to reduce such learning interference and elevate the domain knowledge learning by only manipulating the BN layer. However, the normalization step in BN is intrinsically unstable when the statistics are re-estimated from a few samples. We find that ambiguities can be greatly reduced when only updating the two affine parameters in BN while keeping the source domain statistics. To further enhance the domain knowledge extraction from unlabeled data, we construct an auxiliary branch with label-independent self-supervised learning (SSL) to provide supervision. Moreover, we propose a bi-level optimization based on meta-learning to enforce the alignment of two learning objectives of auxiliary and main branches. The goal is to use the auxiliary branch to adapt the domain and benefit main task for subsequent inference. Our method keeps the same computational cost at inference as the auxiliary branch can be thoroughly discarded after adaptation. Extensive experiments show that our method outperforms the prior works on five WILDS real-world domain shift datasets. Our method can also be integrated with methods with label-dependent optimization to further push the performance boundary. Our code is available at https://github.com/ynanwu/MABN.

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14

Poměnková, J., and R. Maršálek. " Time and frequency domain in the business cycle structure." Agricultural Economics (Zemědělská ekonomika) 58, No. 7 (July 23, 2012): 332–46. http://dx.doi.org/10.17221/113/2011-agricecon.

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 The presented paper deals with the identification of cyclical behaviour of business cycle from the time and frequency domain perspective. Herewith, methods for obtaining the growth business cycle are investigated – the first order difference, the unobserved component models, the regression curves and filtration using the Baxter-King, Christiano-Fitzgerald and Hodrick-Prescott filter. In the case of the time domain, the analysis identification of cycle lengths is based on the dating process of the growth business cycle. Thus, the right and left variant of the naive techniques and the Bry-Boschan algorithm are applied. In the case of the frequency domain, the analysis of the cyclical structure trough spectrum estimate via the periodogram and the autoregressive process are suggested. Results from both domain approaches are compared. On their bases, recommendations for the cyclical structure identification of the growth business cycle of the Czech Republic are formulated. In the time domain analysis, the evaluation of the unity results of detrending techniques from the identification turning point points of view is attached. The analyses are done on the quarterly data of the GDP, the total industry excluding construction, the gross capital formation in 1996–2008 and on the final consumption expenditure in 1995–2008.    

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15

Ciccarelli, Chiara, Hannah Joyce, Jason Robinson, Farhan Nur Kholid, Dominik Hamara, Srabani Kar, and Kun-Rok Jeon. "Terahertz Time-Domain Spectroscopy." Scientific Video Protocols 1, no. 1 (February 1, 2020): 1–4. http://dx.doi.org/10.32386/scivpro.000006.

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Time-Domain terahertz spectroscopy (THz TDS) has attracted attention from many scientific disciplines as it enables accessing the gap between electronic and optical techniques. One application is to probe spintronic dynamics in sub-picosecond time scale. Here, we discuss principles and technical aspects of a typical THz TDS setup. We also show an example of terahertz time-domain data obtained from a Co/Pt thin film calibrant, which is a well-studied spintronic structure emitting strong THz radiation. See video at https://youtu.be/X7vrvQcmy8c.

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16

Murakami, Y. "Time Domain CSMT Method." Exploration Geophysics 19, no. 1-2 (March 1988): 318–21. http://dx.doi.org/10.1071/eg988318.

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17

En-Yuan Sun and W. V. T. Rusch. "Time-domain physical-optics." IEEE Transactions on Antennas and Propagation 42, no. 1 (1994): 9–15. http://dx.doi.org/10.1109/8.272295.

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18

Postema, Gerald J. "Time in Law's Domain." Ratio Juris 31, no. 2 (May 2, 2018): 160–82. http://dx.doi.org/10.1111/raju.12202.

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19

Sokolov, R. T., and J. C. Rogers. "Time-domain cepstral transformations." IEEE Transactions on Signal Processing 41, no. 3 (March 1993): 1161–69. http://dx.doi.org/10.1109/78.205721.

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20

Izydorczyk, J. "Microwave time domain reflectometry." Electronics Letters 41, no. 15 (2005): 848. http://dx.doi.org/10.1049/el:20051696.

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21

Andersen, O. W. "Time domain circuit analysis." IEEE Computer Applications in Power 5, no. 2 (April 1992): 34–38. http://dx.doi.org/10.1109/67.127822.

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22

Yaghjian, Arthur D., and Thorkild B. Hansen. "Time‐domain far fields." Journal of Applied Physics 79, no. 6 (March 15, 1996): 2822–30. http://dx.doi.org/10.1063/1.361274.

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23

Kelkar, P. V., F. Coppinger, A. S. Bhushan, and B. Jalali. "Time-domain optical sensing." Electronics Letters 35, no. 19 (1999): 1661. http://dx.doi.org/10.1049/el:19991116.

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24

Martí-Canales, J., and L. P. Ligthart. "Time domain antenna holography." Electronics Letters 36, no. 6 (2000): 493. http://dx.doi.org/10.1049/el:20000414.

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25

Allen, O. E., D. A. Hill, and A. R. Ondrejka. "Time-domain antenna characterizations." IEEE Transactions on Electromagnetic Compatibility 35, no. 3 (1993): 339–46. http://dx.doi.org/10.1109/15.277308.

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26

Cavicchi, T. J. "DFT time-domain interpolation." IEE Proceedings F Radar and Signal Processing 139, no. 3 (1992): 207. http://dx.doi.org/10.1049/ip-f-2.1992.0025.

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27

Odonde, J. S. O. "Time-domain optimal filtering." Thermochimica Acta 207 (October 1992): 305–12. http://dx.doi.org/10.1016/0040-6031(92)80144-l.

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28

Hui Zhao, Hui Zhao, Lu Tian Lu Tian, Kun Zhao Kun Zhao, Qingli Zhou Qingli Zhou, Yulei Shi Yulei Shi, Dongmei Zhao Dongmei Zhao, Songqing Zhao Songqing Zhao, and Cunlin Zhang Cunlin Zhang. "Identification of pour point depressant by terahertz time-domain spectroscopy." Chinese Optics Letters 9, s1 (2011): s10505–310507. http://dx.doi.org/10.3788/col201109.s10505.

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29

SCIPPA, Antonio, Filippo MONTEVECCHI, Niccolo GROSSI, Lorenzo SALLESE, and Gianni CAMPATELLI. "0605 Time domain simulation model for active fixturing in milling." Proceedings of International Conference on Leading Edge Manufacturing in 21st century : LEM21 2015.8 (2015): _0605–1_—_0605–6_. http://dx.doi.org/10.1299/jsmelem.2015.8._0605-1_.

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30

Lasecki, Walter, Jeffrey Bigham, James Allen, and George Ferguson. "Real-Time Collaborative Planning with the Crowd." Proceedings of the AAAI Conference on Artificial Intelligence 26, no. 1 (September 20, 2021): 2435–36. http://dx.doi.org/10.1609/aaai.v26i1.8419.

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Planning is vital to a wide range of domains, including robotics, military strategy, logistics, itinerary generation and more, that both humans and computers find difficult. Collaborative planning holds the promise of greatly improving performance on these tasks by leveraging the strengths of both humans and automated planners. However, this requires formalizing the problem domain and input, which must be done by hand, a priori, restricting its use in general real-world domains. We propose using a real-time crowd of workers to simultaneously solve the planning problem, formalize the domain, and train an automated system. As plans are developed, the system is able to learn the domain, and contribute larger segments of work.

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31

Zimmermann, K. P. "On frequency-domain and time-domain phase unwrapping." Proceedings of the IEEE 75, no. 4 (1987): 519–20. http://dx.doi.org/10.1109/proc.1987.13759.

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32

Dehler, M., M. Dohlus, and T. Weiland. "Calculating frequency-domain data by time-domain methods." International Journal of Numerical Modelling: Electronic Networks, Devices and Fields 6, no. 1 (February 1993): 19–27. http://dx.doi.org/10.1002/jnm.1660060104.

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33

Gan, Yulu, Yan Bai, Yihang Lou, Xianzheng Ma, Renrui Zhang, Nian Shi, and Lin Luo. "Decorate the Newcomers: Visual Domain Prompt for Continual Test Time Adaptation." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 6 (June 26, 2023): 7595–603. http://dx.doi.org/10.1609/aaai.v37i6.25922.

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Continual Test-Time Adaptation (CTTA) aims to adapt the source model to continually changing unlabeled target domains without access to the source data. Existing methods mainly focus on model-based adaptation in a self-training manner, such as predicting pseudo labels for new domain datasets. Since pseudo labels are noisy and unreliable, these methods suffer from catastrophic forgetting and error accumulation when dealing with dynamic data distributions. Motivated by the prompt learning in NLP, in this paper, we propose to learn an image-layer visual domain prompt for target domains while having the source model parameters frozen. During testing, the changing target datasets can be adapted to the source model by reformulating the input data with the learned visual prompts. Specifically, we devise two types of prompts, i.e., domains-specific prompts and domains-agnostic prompts, to extract current domain knowledge and maintain the domain-shared knowledge in the continual adaptation. Furthermore, we design a homeostasis-based adaptation strategy to suppress domain-sensitive parameters in domain-invariant prompts to learn domain-shared knowledge more effectively. This transition from the model-dependent paradigm to the model-free one enables us to bypass the catastrophic forgetting and error accumulation problems. Experiments show that our proposed method achieves significant performance gains over state-of-the-art methods on four widely-used benchmarks, including CIFAR-10C, CIFAR-100C, ImageNet-C, and VLCS datasets.

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34

Mohammed, Asaad, and Maher K. Mahmood Al-Azawi. "COMPARISON OF TIME AND TIME-FREQUENCY DOMAINS IMPULSIVE NOISE MITIGATION TECHNIQUES FOR POWER LINE COMMUNICATIONS." Journal of Engineering and Sustainable Development 27, no. 1 (January 1, 2023): 68–79. http://dx.doi.org/10.31272/jeasd.27.1.6.

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Impulsive noise is one of the foremost situations in power line communications that degrades the performance of orthogonal frequency division multiplexing used for the power line communications channel. In this paper, a channel version of the broadband power line communications is assumed when evaluating the bit error rate performance. Three impulsive noise environments are assumed, namely heavily, moderately, and weakly disturbed. The well-known time domain mitigation techniques are tested first. These are clipping, blanking, and mixing clipping with blanking. The results of Matlab simulations show that these time-domain mitigation techniques don't significantly improve the bit error rate performance. A hybrid domain of time and frequency mitigation techniques are used to enhance the bit error rate performance. The Matlab simulation results show that this hybrid domain of time and frequency approach outperforms time domain nonlinearities and can largely improve the bit error rate performance. Signal-to-noise ratio gains of about 8 dB, 10 dB, and 10 dB are obtained for heavily, moderately, and weakly disturbed channels, respectively, using the domains of time and frequency mitigation technique at a bit error rate of when compared to the blanking time domain technique.

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35

Elster, C., A. Link, F. Schubert, F. Seifert, M. Walzel, and H. Rinneberg. "Quantitative MRS: comparison of time domain and time domain frequency domain methods using a novel test procedure." Magnetic Resonance Imaging 18, no. 5 (June 2000): 597–606. http://dx.doi.org/10.1016/s0730-725x(00)00140-5.

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36

Ura, Sharifu, and Angkush Kumar Ghosh. "Time Latency-Centric Signal Processing: A Perspective of Smart Manufacturing." Sensors 21, no. 21 (November 4, 2021): 7336. http://dx.doi.org/10.3390/s21217336.

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Smart manufacturing employs embedded systems such as CNC machine tools, programable logic controllers, automated guided vehicles, robots, digital measuring instruments, cyber-physical systems, and digital twins. These systems collectively perform high-level cognitive tasks (monitoring, understanding, deciding, and adapting) by making sense of sensor signals. When sensor signals are exchanged through the abovementioned embedded systems, a phenomenon called time latency or delay occurs. As a result, the signal at its origin (e.g., machine tools) and signal received at the receiver end (e.g., digital twin) differ. The time and frequency domain-based conventional signal processing cannot adequately address the delay-centric issues. Instead, these issues can be addressed by the delay domain, as suggested in the literature. Based on this consideration, this study first processes arbitrary signals in time, frequency, and delay domains and elucidates the significance of delay domain over time and frequency domains. Afterward, real-life signals collected while machining different materials are analyzed using frequency and delay domains to reconfirm its (the delay domain’s) significance in real-life settings. In both cases, it is found that the delay domain is more informative and reliable than the time and frequency domains when the delay is unavoidable. Moreover, the delay domain can act as a signature of a machining situation, distinguishing it (the situation) from others. Therefore, computational arrangements enabling delay domain-based signal processing must be enacted to effectively functionalize the smart manufacturing-centric embedded systems.

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37

Martin, Tina, Konstantin Titov, Andrey Tarasov, and Andreas Weller. "Spectral induced polarization: frequency domain versus time domain laboratory data." Geophysical Journal International 225, no. 3 (February 19, 2021): 1982–2000. http://dx.doi.org/10.1093/gji/ggab071.

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SUMMARY Spectral information obtained from induced polarization (IP) measurements can be used in a variety of applications and is often gathered in frequency domain (FD) at the laboratory scale. In contrast, field IP measurements are mostly done in time domain (TD). Theoretically, the spectral content from both domains should be similar. In practice, they are often different, mainly due to instrumental restrictions as well as the limited time and frequency range of measurements. Therefore, a possibility of transition between both domains, in particular for the comparison of laboratory FD IP data and field TD IP results, would be very favourable. To compare both domains, we conducted laboratory IP experiments in both TD and FD. We started with three numerical models and measurements at a test circuit, followed by several investigations for different wood and sandstone samples. Our results demonstrate that the differential polarizability (DP), which is calculated from the TD decay curves, can be compared very well with the phase of the complex electrical resistivity. Thus, DP can be used for a first visual comparison of FD and TD data, which also enables a fast discrimination between different samples. Furthermore, to compare both domains qualitatively, we calculated the relaxation time distribution (RTD) for all data. The results are mostly in agreement between both domains, however, depending on the TD data quality. It is striking that the DP and RTD results are in better agreement for higher data quality in TD. Nevertheless, we demonstrate that IP laboratory measurements can be carried out in both TD and FD with almost equivalent results. The RTD enables a good comparability of FD IP laboratory data with TD IP field data.

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38

Shi, Yongjie, Xianghua Ying, and Jinfa Yang. "Deep Unsupervised Domain Adaptation with Time Series Sensor Data: A Survey." Sensors 22, no. 15 (July 23, 2022): 5507. http://dx.doi.org/10.3390/s22155507.

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Sensors are devices that output signals for sensing physical phenomena and are widely used in all aspects of our social production activities. The continuous recording of physical parameters allows effective analysis of the operational status of the monitored system and prediction of unknown risks. Thanks to the development of deep learning, the ability to analyze temporal signals collected by sensors has been greatly improved. However, models trained in the source domain do not perform well in the target domain due to the presence of domain gaps. In recent years, many researchers have used deep unsupervised domain adaptation techniques to address the domain gap between signals collected by sensors in different scenarios, i.e., using labeled data in the source domain and unlabeled data in the target domain to improve the performance of models in the target domain. This survey first summarizes the background of recent research on unsupervised domain adaptation with time series sensor data, the types of sensors used, the domain gap between the source and target domains, and commonly used datasets. Then, the paper classifies and compares different unsupervised domain adaptation methods according to the way of adaptation and summarizes different adaptation settings based on the number of source and target domains. Finally, this survey discusses the challenges of the current research and provides an outlook on future work. This survey systematically reviews and summarizes recent research on unsupervised domain adaptation for time series sensor data to provide the reader with a systematic understanding of the field.

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39

KIM, H. H., S. JU, S. CHOI, J. I. PARK, and H. KIM. "An Efficient Time-Domain Electromagnetic Solution Using the Time-Domain Variable Resolution Concept." IEICE Transactions on Communications E89-B, no. 12 (December 1, 2006): 3487–90. http://dx.doi.org/10.1093/ietcom/e89-b.12.3487.

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40

Song, Wonjong, Junan Lee, Nayeon Cho, and Jinwook Burm. "An Ultralow Power Time-Domain Temperature Sensor With Time-Domain Delta–Sigma TDC." IEEE Transactions on Circuits and Systems II: Express Briefs 64, no. 10 (October 2017): 1117–21. http://dx.doi.org/10.1109/tcsii.2015.2503717.

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41

Wang, Zhao-Hong, Yao-Lin Jiang, and Kang-Li Xu. "Time domain and frequency domain model order reduction for discrete time-delay systems." International Journal of Systems Science 51, no. 12 (July 2, 2020): 2134–49. http://dx.doi.org/10.1080/00207721.2020.1785578.

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42

Sun, Qingtao, Qiang Ren, Qiwei Zhan, and Qing Huo Liu. "3-D Domain Decomposition Based Hybrid Finite-Difference Time-Domain/Finite-Element Time-Domain Method With Nonconformal Meshes." IEEE Transactions on Microwave Theory and Techniques 65, no. 10 (October 2017): 3682–88. http://dx.doi.org/10.1109/tmtt.2017.2686386.

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43

MILLER, DAVID F. "A time-domain approach to z-domain model identification." International Journal of Control 44, no. 5 (November 1986): 1285–95. http://dx.doi.org/10.1080/00207178608933670.

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44

Ulriksson, B. "Conversion of frequency-domain data to the time domain." Proceedings of the IEEE 74, no. 1 (1986): 74–77. http://dx.doi.org/10.1109/proc.1986.13405.

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45

Li, Shuangyang, Weijie Yuan, Jinhong Yuan, Baoming Bai, Derrick Wing Kwan Ng, and Lajos Hanzo. "Time-Domain vs. Frequency-Domain Equalization for FTN Signaling." IEEE Transactions on Vehicular Technology 69, no. 8 (August 2020): 9174–79. http://dx.doi.org/10.1109/tvt.2020.3000074.

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46

Zhang, Yudong, Xiqi Li, Ling Wei, Kai Wang, Zhihua Ding, and Guohua Shi. "Time-domain interpolation for Fourier-domain optical coherence tomography." Optics Letters 34, no. 12 (June 10, 2009): 1849. http://dx.doi.org/10.1364/ol.34.001849.

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47

Lin Zhang, Lin Zhang, Chuangjian Cai Chuangjian Cai, Yanlu Lv Yanlu Lv, and and Jianwen Luo and Jianwen Luo. "Early-photon guided reconstruction method for time-domain fluorescence lifetime tomography." Chinese Optics Letters 14, no. 7 (2016): 071702–71706. http://dx.doi.org/10.3788/col201614.071702.

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48

Chattopadhyay, Subhagata. "The Importance of Time-Domain HRV Analysis in Cardiac Health Prediction." Series of Cardiology Research 4, no. 1 (January 1, 2023): 19–23. http://dx.doi.org/10.54178/2768-5985.2022a5.

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Heart rate variability (HRV) is defined as the momentary variation in the end heart rate (EHR) estimated at various intervals (time domains), such as from 2 min (ultra-short HRV) to 24 h (long HRV) intervals. The R peak interval (RRI) between two consecutive beats called momentary heart rate (MHR) provides insight into the impending cardiovascular risk and not the EHR. The autonomic nervous system (ANS) is in charge of maintaining physiological homeostasis by keeping the MHR and in turn the EHR within the normal range of 60–100 bpm. ANS has two components – the sympathetic nervous system (SNS) and the parasympathetic nervous system (PNS). The former increases HR (reduces RRI) while the latter reduces it. Therefore, the RR time-domain-HRV-data (THD) provides better insight into overall health than the EHR. Six types of THDs, e.g., mean-HR, mean-RR, SDNN, SDHR, RMSSD, and pNN50 are discussed in this article.

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Liu, Quande, Cheng Chen, Qi Dou, and Pheng-Ann Heng. "Single-Domain Generalization in Medical Image Segmentation via Test-Time Adaptation from Shape Dictionary." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 2 (June 28, 2022): 1756–64. http://dx.doi.org/10.1609/aaai.v36i2.20068.

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Abstract:

Domain generalization typically requires data from multiple source domains for model learning. However, such strong assumption may not always hold in practice, especially in medical field where the data sharing is highly concerned and sometimes prohibitive due to privacy issue. This paper studies the important yet challenging single domain generalization problem, in which a model is learned under the worst-case scenario with only one source domain to directly generalize to different unseen target domains. We present a novel approach to address this problem in medical image segmentation, which extracts and integrates the semantic shape prior information of segmentation that are invariant across domains and can be well-captured even from single domain data to facilitate segmentation under distribution shifts. Besides, a test-time adaptation strategy with dual-consistency regularization is further devised to promote dynamic incorporation of these shape priors under each unseen domain to improve model generalizability. Extensive experiments on two medical image segmentation tasks demonstrate the consistent improvements of our method across various unseen domains, as well as its superiority over state-of-the-art approaches in addressing domain generalization under the worst-case scenario.

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Lehrenfeld, Christoph, and Maxim Olshanskii. "An Eulerian finite element method for PDEs in time-dependent domains." ESAIM: Mathematical Modelling and Numerical Analysis 53, no. 2 (March 2019): 585–614. http://dx.doi.org/10.1051/m2an/2018068.

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Abstract:

The paper introduces a new finite element numerical method for the solution of partial differential equations on evolving domains. The approach uses a completely Eulerian description of the domain motion. The physical domain is embedded in a triangulated computational domain and can overlap the time-independent background mesh in an arbitrary way. The numerical method is based on finite difference discretizations of time derivatives and a standard geometrically unfitted finite element method with an additional stabilization term in the spatial domain. The performance and analysis of the method rely on the fundamental extension result in Sobolev spaces for functions defined on bounded domains. This paper includes a complete stability and error analysis, which accounts for discretization errors resulting from finite difference and finite element approximations as well as for geometric errors coming from a possible approximate recovery of the physical domain. Several numerical examples illustrate the theory and demonstrate the practical efficiency of the method.

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