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Journal articles on the topic 'Time interval measurement'

Author: Grafiati

Published: 4 June 2021

Last updated: 13 February 2022

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1

Ingham, Roger J., Anne K. Cordes, and Merrilyn L. Gow. "Time-Interval Measurement of Stuttering." Journal of Speech, Language, and Hearing Research 36, no. 3 (June 1993): 503–15. http://dx.doi.org/10.1044/jshr.3603.503.

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This paper reports the results of two experiments that investigated interval-by-interval inter and intrajudge agreement for stuttered and nonstuttered speech intervals (4.0 sec). The first experiment demonstrated that interval-by-interval interjudge agreement could be significantly improved, and to satisfactory levels, by training judges to discriminate between experimenter-agreed intervals of stuttered and nonstuttered speech. The findings also showed that, independent of training, judges with relatively high intrajudge agreement also showed relatively higher interjudge agreement. The second experiment showed that interval-by-interval interjudge agreement was not significantly different if judges rated 4-sec speech intervals from different samples under three conditions: in random order, separated by 5-sec recording intervals; in correct order, also separated by 5-sec recording intervals; or after brief judgment signals that occurred every 4 sec during continuous samples. The implications of these findings for stuttering measurement are discussed.

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2

Cordes, Anne K., and Roger J. Ingham. "Time-Interval Measurement of Stuttering." Journal of Speech, Language, and Hearing Research 37, no. 4 (August 1994): 779–88. http://dx.doi.org/10.1044/jshr.3704.779.

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The study reported in this article used a binary forced-choice judgment procedure to investigate the effects of sample duration on observers’ judgments of stuttering. Two groups of judges, differing in their previous experience with stuttering, categorized 270 speech intervals as stuttered or nonstuttered; the intervals were drawn from 30 persons who stuttered and ranged from 1 sec to 15 sec in duration. Results showed that judgments were consistently related to interval duration, with shorter intervals significantly more likely than longer intervals to be labeled nonstuttered. Interjudge agreement levels, however, were largely unaffected by the different interval durations for most speakers and for both judge groups, with the exception of the longest and shortest intervals drawn from speakers evidencing the mildest and most severe stuttering. An interval duration in the 3- to 5-sec region appeared to attract the most satisfactory level of agreement. The implications of these findings for interval-based clinical and experimental measurements of stuttering are discussed.

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3

Ingham, Roger J., Anne K. Cordes, and Patrick Finn. "Time-Interval Measurement of Stuttering." Journal of Speech, Language, and Hearing Research 36, no. 6 (December 1993): 1168–76. http://dx.doi.org/10.1044/jshr.3606.1168.

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The study reported in this paper was designed to replicate and extend the results of an earlier study (Ingham, Cordes, & Gow, 1993) that investigated time-interval judgments of stuttering. Results confirmed earlier findings that interjudge agreement is higher for these interval-recording tasks than has been previously reported for event-based analyses of stuttering judgments or for time-interval analyses of event judgments. Results also confirmed an earlier finding that judges with intrajudge agreement levels of 90% or better show higher interjudge agreement than judges with lower intrajudge agreement scores. This study failed to find differences between audiovisual and audio-only judgment conditions; between relatively experienced and relatively inexperienced student judges; and, most importantly, between the judgments made, and the agreement levels achieved, by judges from two different clinical research settings. The implications of these findings for attempts to develop a reliable measurement method for stuttering are discussed.

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Cordes, Anne K., and Roger J. Ingham. "Time-Interval Measurement of Stuttering." Journal of Speech, Language, and Hearing Research 37, no. 6 (December 1994): 1295–307. http://dx.doi.org/10.1044/jshr.3706.1295.

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5

Johnson, Reg H., and Paul Cummings. "Microwave Attenuation Measurement by Time-Interval Ratio." IEEE Transactions on Instrumentation and Measurement IM-34, no. 4 (December 1985): 602–6. http://dx.doi.org/10.1109/tim.1985.4315417.

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6

Arkani, Mohammad, and Gholamreza Raisali. "Measurement of dead time by time interval distribution method." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 774 (February 2015): 151–58. http://dx.doi.org/10.1016/j.nima.2014.11.069.

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7

Keranen, Pekka, and Juha Kostamovaara. "Oscillator Instability Effects in Time Interval Measurement." IEEE Transactions on Circuits and Systems I: Regular Papers 60, no. 7 (July 2013): 1776–86. http://dx.doi.org/10.1109/tcsi.2012.2230502.

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8

Hamza, Gihan G. "Time Interval Measurement in the Picosecond Accuracy." Instruments and Experimental Techniques 62, no. 6 (November 2019): 778–84. http://dx.doi.org/10.1134/s0020441220010121.

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9

Zhan, Hui Qin, Shuo Li, and Jun Gu. "Double Interpolation Method in Time-Interval Measurement." Advanced Materials Research 753-755 (August 2013): 2153–58. http://dx.doi.org/10.4028/www.scientific.net/amr.753-755.2153.

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Analog interpolation is the key technology of high-resolution short time-interval measurement. This study proposes a double interpolation method in time-interval measurement. The technique greatly improves the resolution of the analog time expander, which is based on the principle of charging and discharging of the capacitor. This method can significantly reduce the interpolation time, effectively solve the conflict between high measurement resolution and fast measurement rate. The principle of the time measurement expander based on double interpolation method is described in this paper and compared with the expander based on single interpolation method. Its measurement error is also analyzed in this study.

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10

Grzelak, Sławomir, Marcin Kowalski, Jarosław Czoków, and Marek Zieliński. "HIGH RESOLUTION TIME-INTERVAL MEASUREMENT SYSTEMS APPLIED TO FLOW MEASUREMENT." Metrology and Measurement Systems 21, no. 1 (March 1, 2014): 77–84. http://dx.doi.org/10.2478/mms-2014-0008.

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Abstract The designing process of high resolution time interval measurement systems creates many problems that need to be eliminated. The problems are: the latch error, the nonlinearity conversion, the different duty cycle coefficient of the clock signal, and the clock signal jitter. Factors listed above affect the result of measurement. The FPGA (Field Programmable Gate Array) structure also imposes some restrictions, especially when a tapped delay line is constructed. The article describes the high resolution time-to-digital converter, implemented in a FPGA structure, and the types of errors that appear there. The method of minimization and processing of data to reduce the influence of errors on the measurement is also described.

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11

Igarashi, R., S. Fukushima, Y. Yamada, T. Uemura, A. Saitoh, and Y. Narita. "Evaluation of fluctuating dead times by time interval distribution measurement." IEEE Transactions on Instrumentation and Measurement 43, no. 2 (April 1994): 164–69. http://dx.doi.org/10.1109/19.293414.

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12

Cordes, Anne K., and Roger J. Ingham. "Effects of Time-Interval Judgment Training on Real-Time Measurement of Stuttering." Journal of Speech, Language, and Hearing Research 42, no. 4 (August 1999): 862–79. http://dx.doi.org/10.1044/jslhr.4204.862.

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The purpose of this study was to investigate whether a previously developed interval-based training program could improve judges' stuttering event judgments. Two groups of judges made real-time stuttering event judgments (computer-mouse button presses) in 3 to 6 trials before the response-contingent judgment training program and in another 3 to 6 trials after training, for recordings of 9 adults who stuttered. Their judgments were analyzed in terms of number of stuttering events, duration of stuttering, and 5-s intervals of speech that could be categorized as judged (or not judged) to contain stuttering. Results showed (a) changes in the amount of stuttering identified by the judges; (b) improved correspondence between the judges' identifications of stuttering events and interval-based standards previously developed from judgments made by experienced, authoritative judges; (c) improved correspondence between interval-based analyses of the judges' stuttering judgments and the previously developed standards; (d) improved intrajudge agreement; (e) improved interjudge agreement; and (f) convergence between the 2 judge groups, for samples and speakers used during training tasks and also for other speakers. Some implications of these findings for developing standardized procedures for the real-time measurement of stuttering are discussed.

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13

Panek, P. "Time-Interval Measurement Based on SAW Filter Excitation." IEEE Transactions on Instrumentation and Measurement 57, no. 11 (November 2008): 2582–88. http://dx.doi.org/10.1109/tim.2008.925014.

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14

Cordes, Anne K., and Roger J. Ingham. "Time-Interval Measurement of Stuttering: Establishing and Modifying Judgment Accuracy." Journal of Speech, Language, and Hearing Research 39, no. 2 (April 1996): 298–310. http://dx.doi.org/10.1044/jshr.3902.298.

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The purpose of this study was to determine whether accuracy training for interval judgments of stuttering might generalize to increased accuracy and/or interjudge agreement for intervals other than those used during training. Ten upper-division speech-language pathology students judged 5-s audiovisually recorded speech intervals as stuttered or nonstuttered in a series of group and single-subject experiments. Judgment accuracy was determined with respect to judgments provided previously by 10 recognized authorities on stuttering and its treatment. Training occurred within single-subject experiments that used multiple baselines across speakers and repeated generalization probes to assess training effects. Results showed that judgment accuracy tended to increase after training for speakers used during the training process as well as for unfamiliar speakers. Results also replicated previous findings of slight increases in interjudge and intrajudge agreement after interval-judgment training. The implications of these results for developing a valid and reliable stuttering measurement system are discussed.

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15

Zielinski, Marek, Dariusz Chaberski, Maciej Gurski, and Marcin Kowalski. "Multi tapped delay line time-interval measurement system implemented in a programmable structure." ACTA IMEKO 3, no. 3 (September 23, 2014): 43. http://dx.doi.org/10.21014/acta_imeko.v3i3.57.

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This paper describes a time-interval measurement system with increased resolution using multiple taped delay lines. In this time-interval measurement system, sixteen time-stamps are registered during a single measuring cycle (one shot). It means that the value of the measured time-interval can be interpolated with higher resolution without increasing the number of measurements or the interpolation time. Limiting the total measurement time reduces the energy consumption which is particularly important in battery powered systems.

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16

Wang, Xin Gang, Fei Wang, and Hai Gang Yang. "A Vernier Delay Line for Time Interval Measurement." Advanced Materials Research 301-303 (July 2011): 995–1000. http://dx.doi.org/10.4028/www.scientific.net/amr.301-303.995.

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This paper presents a Vernier Delay Line (VDL) for time interval measurement. A dedicated multiplexer is inserted into each stage of the proposed VDL. As a result, the D-flip-flops in each stage can be served as a large delay cell as well as a traditional arbiter. Moreover, the proposed interface circuit can save time residue out for further fine granularity measurement. Experimental results show that the proposed VDL achieves a 30ns measurement range with 6600 transistors.

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17

Yu, Hua. "Short Time Interval Measurement Method Based on Compensation." Applied Mechanics and Materials 608-609 (October 2014): 865–69. http://dx.doi.org/10.4028/www.scientific.net/amm.608-609.865.

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In this paper, we use a compensation method of the measured short interval to improve the measurement accuracy by two groups of multiplex. By using two groups of multi-channel monostable pulse signal as a compensation signal, we respectively compensate time difference between the starting/ending measured short time interval and the reference signal. Pulse width of each compensation signal is uniform increasing and incremental sum is a reference signal cycle. We select the average value of two compensation signals pulse width as the beginning and the end of the time difference when the reference signal value jumps after compensation. Then according to the number of reference signal and two compensation time difference we can obtain measured short time interval. Error analysis and evaluation of uncertainty measurement show that the precision of this method is decided by pulse width increment of adjacent compensation signals. The experimental data shows that the method effectively reduces the measurement error of one reference signal cycle in the measurement of time interval.

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18

Tan, S. K., and Y. M. Chiew. "Point‐Rainfall Measurement in Terms of Time Interval." Journal of Hydraulic Engineering 117, no. 10 (October 1991): 1304–9. http://dx.doi.org/10.1061/(asce)0733-9429(1991)117:10(1304).

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19

Mirri, Domenico, Gaetano Pasini, Gaetano Iuculano, Fabio Filicori, Gabriella Pellegrini, and Andrea Gabrielli. "A high-resolution digital time-interval measurement instrument." Measurement 22, no. 3-4 (November 1997): 129–40. http://dx.doi.org/10.1016/s0263-2241(98)00005-0.

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20

SCHMIDT, J. M., and J. J. B. SMITH. "Short Interval Time Measurement by a Parasitoid Wasp." Science 237, no. 4817 (August 21, 1987): 903–5. http://dx.doi.org/10.1126/science.237.4817.903.

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21

Rashidzadeh, R., R. Muscedere, M. Ahmadi, and W. C. Miller. "A Delay Generation Technique for Narrow Time Interval Measurement." IEEE Transactions on Instrumentation and Measurement 58, no. 7 (July 2009): 2245–52. http://dx.doi.org/10.1109/tim.2009.2013685.

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22

Remesan, Renji, Azadeh Ahmadi, Muhammad Ali Shamim, and Dawei Han. "Effect of data time interval on real-time flood forecasting." Journal of Hydroinformatics 12, no. 4 (February 2, 2010): 396–407. http://dx.doi.org/10.2166/hydro.2010.063.

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Rainfall–runoff is a complicated nonlinear process and many data mining tools have demonstrated their powerful potential in its modelling but still there are many unsolved problems. This paper addresses a mostly ignored area in hydrological modelling: data time interval for models. Modern data collection and telecommunication technologies can provide us with very high resolution data with extremely fine sampling intervals. We hypothesise that both too large and too small time intervals would be detrimental to a model's performance, which has been illustrated in the case study. It has been found that there is an optimal time interval which is different from the original data time interval (i.e. the measurement time interval). It has been found that the data time interval does have a major impact on the model's performance, which is more prominent for longer lead times than for shorter ones. This is highly relevant to flood forecasting since a flood modeller usually tries to stretch his/her model's lead time as far as possible. If the selection of data time interval is not considered, the model developed will not be performing at its full potential. The application of the Gamma Test and Information Entropy introduced in this paper may help the readers to speed up their data input selection process.

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23

Sun, Wan-Li, Chih-Yao Chang, Nai-Chueh Wang, and Chung-Yee Leung. "A time-to-amplitude conversion chain (TACC) for time interval measurement." Measurement Science and Technology 11, no. 3 (January 26, 2000): N36—N38. http://dx.doi.org/10.1088/0957-0233/11/3/402.

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24

Ti, Xiaonan, Naoki Tani, Minoru Isobe, and Hidenori Kai. "Time-measurement-regulating peptide PIN may alter a timer conformation of Time Interval Measuring Enzyme (TIME)." Journal of Insect Physiology 52, no. 5 (May 2006): 461–67. http://dx.doi.org/10.1016/j.jinsphys.2005.12.005.

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25

Smith, Brian L., and Jared M. Ulmer. "Freeway Traffic Flow Rate Measurement: Investigation into Impact of Measurement Time Interval." Journal of Transportation Engineering 129, no. 3 (May 2003): 223–29. http://dx.doi.org/10.1061/(asce)0733-947x(2003)129:3(223).

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26

Gu, Xinyu, Ji Guo, Shaohua Shi, and Xiaohui Li. "Development of precise multi-channel time-interval measurement system." JOURNAL OF ELECTRONIC MEASUREMENT AND INSTRUMENT 27, no. 1 (November 29, 2013): 69–75. http://dx.doi.org/10.3724/sp.j.1187.2013.00069.

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27

Grzęda, Grzegorz, and Ryszard Szplet. "Time interval measurement module implemented in SoC FPGA device." International Journal of Electronics and Telecommunications 62, no. 3 (September 1, 2016): 237–46. http://dx.doi.org/10.1515/eletel-2016-0032.

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Abstract We presents the design and test results of a picosecond-precision time interval measurement module, integrated as a System-on-Chip in an FPGA device. Implementing a complete measurement instrument of a high precision in one chip with the processing unit gives an opportunity to cut down the size of the final product and to lower its cost. Such approach challenges the constructor with several design issues, like reduction of voltage noise, propagating through power lines common for the instrument and processing unit, or establishing buses efficient enough to transport mass measurement data. The general concept of the system, design hierarchy, detailed hardware and software solutions are presented in this article. Also, system test results are depicted with comparison to traditional ways of building a measurement instrument.

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28

Gomah, Gihan. "A traceable time interval measurement with a reduced uncertainty." International Journal of Metrology and Quality Engineering 6, no. 3 (2015): 301. http://dx.doi.org/10.1051/ijmqe/2015009.

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29

Salomon, R., and R. Joost. "BOUNCE: A New High-Resolution Time-Interval Measurement Architecture." IEEE Embedded Systems Letters 1, no. 2 (August 2009): 56–59. http://dx.doi.org/10.1109/les.2009.2034711.

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30

De Angelis, Alessio, Guido De Angelis, and Paolo Carbone. "Using Gaussian-Uniform Mixture Models for Robust Time-Interval Measurement." IEEE Transactions on Instrumentation and Measurement 64, no. 12 (December 2015): 3545–54. http://dx.doi.org/10.1109/tim.2015.2469434.

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31

Shi, Shao-Hua, Xiao-Hui Li, Hui-Jun Zhang, and Zhi-Xiong Zhao. "Development of a Multi-Channel Time Interval Measurement for Time Standard Assembly." Sensor Letters 9, no. 4 (August 1, 2011): 1482–84. http://dx.doi.org/10.1166/sl.2011.1657.

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32

Bao-Qiang, Du, and Zhou Wei. "Super-High Resolution Time Interval Measurement Method Based on Time-Space Relationships." Chinese Physics Letters 26, no. 10 (September 29, 2009): 100601. http://dx.doi.org/10.1088/0256-307x/26/10/100601.

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33

Wang, Wei, Guohua Liu, and Dingjia Liu. "Chebyshev Similarity Match between Uncertain Time Series." Mathematical Problems in Engineering 2015 (2015): 1–13. http://dx.doi.org/10.1155/2015/105128.

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In real application scenarios, the inherent impreciseness of sensor readings, the intentional perturbation of privacy-preserving transformations, and error-prone mining algorithms cause much uncertainty of time series data. The uncertainty brings serious challenges for the similarity measurement of time series. In this paper, we first propose a model of uncertain time series inspired by Chebyshev inequality. It estimates possible sample value range and central tendency range in terms of sample estimation interval and central tendency estimation interval, respectively, at each time slot. In comparison with traditional models adopting repeated measurements and random variable, Chebyshev model reduces overall computational cost and requires no prior knowledge. We convert Chebyshev uncertain time series into certain time series matrix; therefore noise reduction and dimensionality reduction are available for uncertain time series. Secondly, we propose a new similarity matching method based on Chebyshev model. It depends on overlaps between two sample estimation intervals and overlaps between central tendency estimation intervals from different uncertain time series. At the end of this paper, we conduct an extensive experiment and analyze the results by comparing with prior works.

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34

Yoshioka, Takeo, Atsushi Korenaga, Hiroki Mano, and Takashi Yamamoto. "Diagnosis of Rolling Bearing by Measuring Time Interval of AE Generation." Journal of Tribology 121, no. 3 (July 1, 1999): 468–72. http://dx.doi.org/10.1115/1.2834091.

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We have developed a new method for measuring time intervals of Acoustic Emission (AE) generation for diagnosis of a radial rolling bearing. The method makes the AE signal itself a trigger of the oscillation of the clock pulse and measures the time interval of AE generation by integration of the clock pulses. The measurement device consists of the threshold, clock, time interval measurement and memory circuit, and was applied to rolling contact fatigue experiments. It was confirmed by the experiments that the measured time intervals of AE generation on the inner raceway or the ball agreed with the value calculated based on the kinetics of the rolling bearing. Moreover, we could identify the elements in which a fatigue crack was propagating by the method before the spalling appeared. The identified elements agreed with the failed elements.

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35

Schmeiser, Hato, and Daliana Luca. "The impact of time discretization on solvency measurement." Journal of Risk Finance 18, no. 1 (January 16, 2017): 2–20. http://dx.doi.org/10.1108/jrf-07-2016-0089.

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Purpose The purpose of this paper is to study how the discretization interval affects the solvency measurement of a property-liability insurance company. Design/methodology/approach Starting with a basic solvency model, the authors study the impact of the discretization interval on risk measures. The analysis considers the sensitivity of the discrepancy between the risk measures in continuous and discrete time to various parameters, such as the asset-to-liability ratio, the characteristics of the asset and liability processes, as well as the correlation between assets and liabilities. Capital requirements for the one-year planning horizon in continuous vs discrete time are reported as well. The purpose is to report the degree to which the deviations in risk measures, due to the different discretization intervals, can be reduced by means of increasing the frequency with which the risk measures are assessed. Findings The simulation results suggest that the risk measures of an insurance company are consistently underestimated when assessed on an annual basis (as it is currently done under insurance regulation such as Solvency II). The authors complement the analysis with the capital requirements of an insurance company and conclude that more frequent discretization translates into higher capital requirements for the insurance company. Both the probability of ruin and the expected policyholder deficit (EPD) can be reduced through intermediate financial reports. Originality/value The results from our simulation analysis suggest that that the choice of discretization interval has an impact on the risk assessment of an insurance company which uses the probability of ruin and the EPD as risk measures. By assessing the risk measures once a year, both risk measures and the capital requirements are consistently underestimated. Therefore, the recommendation for risk managers is to complement the capital requirements in solvency regulation with sensitivity analyses of the risk measures presented with respect to time discretization. On the one hand, it seems to us that there is value in knowing about the substantial discrepancy between the focused time discrete ruin probability and EPD compared to the continuous version. On the other hand, and if there are no substantial transaction costs associated with more frequent monitoring of solvency figures, a frequent update would be helpful to increase the accuracy of the calculations and reduce the EPD.

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36

Chen Ruiqiang, 陈瑞强, and 江月松 Jiang Yuesong. "Method of Measurement on Time-Interval in Pulsed Laser Ranging." Acta Optica Sinica 33, no. 2 (2013): 0212004. http://dx.doi.org/10.3788/aos201333.0212004.

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37

Zhao, Jun, Zhiliang Zhao, and Li Fu. "Research on the High Resolution Precision Time-interval Measurement Methods." Procedia Engineering 174 (2017): 1257–61. http://dx.doi.org/10.1016/j.proeng.2017.01.298.

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38

van Etten, W. "Distance determination by means of accurate, periodic time interval measurement." IEEE Transactions on Instrumentation and Measurement 37, no. 1 (March 1988): 155–57. http://dx.doi.org/10.1109/19.2689.

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39

Abbaspour, Madjid, Rostam Golmohammadi, Parvin Nassiri, and Hossien Mahjub. "An investigation on time-interval optimisation of traffic noise measurement." Noise Notes 6, no. 3 (July 2007): 9–16. http://dx.doi.org/10.1260/147547307782218941.

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40

Chen, P., Shen-Luan Liu, and Jingshown Wu. "A CMOS pulse-shrinking delay element for time interval measurement." IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing 47, no. 9 (2000): 954–58. http://dx.doi.org/10.1109/82.868466.

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41

张, 炜. "A Time Interval Measurement Circuit Based on Delay Line Interpolation." Open Journal of Circuits and Systems 04, no. 01 (2015): 8–14. http://dx.doi.org/10.12677/ojcs.2015.41002.

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42

Abbaspour, Madjid, Rostam Golmohammadi, Parvin Nassiri, and Hossien Mahjub. "An Investigation on Time-Interval Optimisation of Traffic Noise Measurement." Journal of Low Frequency Noise, Vibration and Active Control 25, no. 4 (December 2006): 267–73. http://dx.doi.org/10.1260/026309206779884883.

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43

Liguori, Consolatina, Alessandro Ruggiero, Domenico Russo, and Paolo Sommella. "Estimation of the minimum measurement time interval in acoustic noise." Applied Acoustics 127 (December 2017): 126–32. http://dx.doi.org/10.1016/j.apacoust.2017.05.032.

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44

Hamza, Gihan Gomah. "A Study on the EURAMET Comparison of Time Interval Measurement." MAPAN 29, no. 3 (February 1, 2014): 207–12. http://dx.doi.org/10.1007/s12647-014-0095-z.

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Hamza, Gihan Gomah. "Investigation of the Optimum Trigger Level in Time Interval Measurement." MAPAN 29, no. 4 (May 1, 2014): 255–60. http://dx.doi.org/10.1007/s12647-014-0102-4.

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46

Xiangwei Zhu, Guangfu Sun, Shaowei Yong, and Zhaowen Zhuang. "A High-Precision Time Interval Measurement Method Using Phase-Estimation Algorithm." IEEE Transactions on Instrumentation and Measurement 57, no. 11 (November 2008): 2670–76. http://dx.doi.org/10.1109/tim.2008.925025.

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47

Panek, P. "Random Errors in Time Interval Measurement Based on SAW Filter Excitation." IEEE Transactions on Instrumentation and Measurement 57, no. 6 (June 2008): 1244–50. http://dx.doi.org/10.1109/tim.2007.915465.

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48

Coates, P. B. "Analytical corrections for dead time effects in the measurement of time‐interval distributions." Review of Scientific Instruments 63, no. 3 (March 1992): 2084–88. http://dx.doi.org/10.1063/1.1143170.

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49

Limi Jaya, Gibran, Shoushun Chen, and Liter Siek. "The Design of Clocked-Comparator-Based Time-Interval Measurement Circuit for Pulse ToF Measurement." IEEE Sensors Journal 17, no. 20 (October 15, 2017): 6699–706. http://dx.doi.org/10.1109/jsen.2017.2749417.

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50

Flavell, Carol A., Laurence G. Marshman, and Susan J. Gordon. "Measurement of transversus abdominis activation in chronic low back pain patients using a novel standardized real-time ultrasound imaging method." Ultrasound 27, no. 1 (July 5, 2018): 31–37. http://dx.doi.org/10.1177/1742271x18785403.

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Abstract:

Real-time ultrasound imaging (US) to measure abdominal muscle dimensions has aided low back pain rehabilitation and research. Notwithstanding, ultrasound imaging measurement of transversus abdominis muscle activation in chronic low back pain populations has been characterized by variable and generally suboptimal intra-observer reliability. Methodological deficiencies of ‘freehand’ ultrasound imaging are uncontrolled probe–skin pressure, inclination and roll of the probe. Despite previous attempts to standardize these parameters, intra-observer reliability in chronic low back pain was poor to moderate (0.32–0.62). Therefore, a standardized method that controls and records probe force, inclination and roll during ultrasound imaging may optimize measurement reliability in chronic low back pain. This pilot study investigated utility, standardization and intra-observer reliability of ultrasound imaging transversus abdominis thickness measurement in chronic low back pain patients (n = 17). Transversus abdominis imaging over two separate measurement sessions was conducted using a novel method to standardize probe parameters. Resting and contracted transversus abdominis thickness, and transversus abdominis activation measurements were obtained from duplicate paired images (n = 68). Intra-class correlation coefficients were reported with 95% confidence intervals. Transversus abdominis thickness at rest (intra-class correlation coefficient = 0.97 confidence interval: 0.93, 0.99), when contracted (intra-class correlation coefficient = 0.99 confidence interval: 0.97, 0.99) and transversus abdominis activation (intra-class correlation coefficient = 0.93 confidence interval: 0.81, 0.97) measurements were highly reliable. Ultrasound imaging of transversus abdominis using the novel standardized ultrasound imaging method produced highly reliable intra-observer transversus abdominis measurements, superior to ‘freehand’ ultrasound imaging, despite the physical limitations typically associated with a chronic low back pain population. Unique standardizing ranges for ‘probe force device’ probe parameters were obtained. This novel standardized ultrasound imaging method may optimize transversus abdominis activation assessment in chronic low back pain and other populations, aiding future research.

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