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AbstractThis article presents an overview of the single-channel dereverberation methods suitable for distant speech recognition (DSR) application. The dereverberation methods are mainly classified based on the domain of enhancement of speech signal captured by a distant microphone. Many single-channel speech enhancement methods focus on either denoising or dereverberating the distorted speech signal. There are very few methods that consider both noise and reverberation effects. Such methods are discussed under a multistage approach in this article. The article concludes with a hypothesis that the methods that do not require an a priori reverberation impulse response is desirable in varying the environmental conditions for DSR applications such as intelligent home and office environments, humanoid robots, and automobiles rather than the methods that require an a priori reverberation impulse response.
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Guerrero Flores, Cristina, Georgina Tryfou, and Maurizio Omologo. "Cepstral distance based channel selection for distant speech recognition." Computer Speech & Language 47 (January 2018): 314–32. http://dx.doi.org/10.1016/j.csl.2017.08.003.
Nematollahi, Mohammad Ali, and S. A. R. Al-Haddad. "Distant Speaker Recognition: An Overview." International Journal of Humanoid Robotics 13, no. 02 (May 25, 2016): 1550032. http://dx.doi.org/10.1142/s0219843615500322.
Distant speaker recognition (DSR) system assumes the microphones are far away from the speaker’s mouth. Also, the position of microphones can vary. Furthermore, various challenges and limitation in terms of coloration, ambient noise and reverberation can bring some difficulties for recognition of the speaker. Although, applying speech enhancement techniques can attenuate speech distortion components, it may remove speaker-specific information and increase the processing time in real-time application. Currently, many efforts have been investigated to develop DSR for commercial viable systems. In this paper, state-of-the-art techniques in DSR such as robust feature extraction, feature normalization, robust speaker modeling, model compensation, dereverberation and score normalization are discussed to overcome the speech degradation components i.e., reverberation and ambient noise. Performance results on DSR show that whenever speaker to microphone distant increases, recognition rates decreases and equal error rate (EER) increases. Finally, the paper concludes that applying robust feature and robust speaker model varying lesser with distant, can improve the DSR performance.
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Swietojanski, Pawel, Arnab Ghoshal, and Steve Renals. "Convolutional Neural Networks for Distant Speech Recognition." IEEE Signal Processing Letters 21, no. 9 (September 2014): 1120–24. http://dx.doi.org/10.1109/lsp.2014.2325781.
Pardede, Hilman F., Asri R. Yuliani, and Rika Sustika. "Convolutional Neural Network and Feature Transformation for Distant Speech Recognition." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 6 (December 1, 2018): 5381. http://dx.doi.org/10.11591/ijece.v8i6.pp5381-5388.
In many applications, speech recognition must operate in conditions where there are some distances between speakers and the microphones. This is called distant speech recognition (DSR). In this condition, speech recognition must deal with reverberation. Nowadays, deep learning technologies are becoming the the main technologies for speech recognition. Deep Neural Network (DNN) in hybrid with Hidden Markov Model (HMM) is the commonly used architecture. However, this system is still not robust against reverberation. Previous studies use Convolutional Neural Networks (CNN), which is a variation of neural network, to improve the robustness of speech recognition against noise. CNN has the properties of pooling which is used to find local correlation between neighboring dimensions in the features. With this property, CNN could be used as feature learning emphasizing the information on neighboring frames. In this study we use CNN to deal with reverberation. We also propose to use feature transformation techniques: linear discriminat analysis (LDA) and maximum likelihood linear transformation (MLLT), on mel frequency cepstral coefficient (MFCC) before feeding them to CNN. We argue that transforming features could produce more discriminative features for CNN, and hence improve the robustness of speech recognition against reverberation. Our evaluations on Meeting Recorder Digits (MRD) subset of Aurora-5 database confirm that the use of LDA and MLLT transformations improve the robustness of speech recognition. It is better by 20% relative error reduction on compared to a standard DNN based speech recognition using the same number of hidden layers.
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NAKANO, Alberto Yoshihiro, Seiichi NAKAGAWA, and Kazumasa YAMAMOTO. "Distant Speech Recognition Using a Microphone Array Network." IEICE Transactions on Information and Systems E93-D, no. 9 (2010): 2451–62. http://dx.doi.org/10.1587/transinf.e93.d.2451.
Matassoni, Marco, Maurizio Omologo, Diego Giuliani, and Piergiorgio Svaizer. "Hidden Markov model training with contaminated speech material for distant-talking speech recognition." Computer Speech & Language 16, no. 2 (April 2002): 205–23. http://dx.doi.org/10.1006/csla.2002.0191.
Mustafaev, M. Sh, V. A. Vissarionov, E. M. Tarchokova, and S. A. Dyshekova. "Basics of complex rehabilitation of patients with speech disorders after uranoplasty." Medical alphabet, no. 3 (June 12, 2020): 40–42. http://dx.doi.org/10.33667/2078-5631-2020-3-40-42.
Result of the study a theoretical overview technologies for eliminating cleft of the palate and their distant results for the formation of correct speech. The process of speech formation and the influence of technical features of first and reconstructive uranoplastics using pharyngeal flap on this process was analyzed.
Dissertations / Theses on the topic "Distant speech":
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Ravanelli, Mirco. "Deep Learning for Distant Speech Recognition." Doctoral thesis, Università degli studi di Trento, 2017. https://hdl.handle.net/11572/368959.
Deep learning is an emerging technology that is considered one of the most promising directions for reaching higher levels of artificial intelligence.
Among the other achievements, building computers that understand speech represents a crucial leap towards intelligent machines. Despite the great efforts of the past decades, however, a natural and robust human-machine speech interaction still appears to be out of reach, especially when users interact with a distant microphone in noisy and reverberant environments. The latter disturbances severely hamper the intelligibility of a speech signal, making Distant Speech Recognition (DSR) one of the major open challenges in the field.
This thesis addresses the latter scenario and proposes some novel techniques, architectures, and algorithms to improve the robustness of distant-talking acoustic models. We first elaborate on methodologies for realistic data contamination, with a particular emphasis on DNN training with simulated data. We then investigate on approaches for better exploiting speech contexts, proposing some original methodologies for both feed-forward and recurrent neural networks. Lastly, inspired by the idea that cooperation across different DNNs could be the key for counteracting the harmful effects of noise and reverberation, we propose a novel deep learning paradigm called “network of deep neural networks†.
The analysis of the original concepts were based on extensive experimental validations conducted on both real and simulated data, considering different corpora, microphone configurations, environments, noisy conditions, and ASR tasks.
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Ravanelli, Mirco. "Deep Learning for Distant Speech Recognition." Doctoral thesis, University of Trento, 2017. http://eprints-phd.biblio.unitn.it/2693/1/PhD_thesis_Ravanelli_Mirco_archived.pdf.
Deep learning is an emerging technology that is considered one of the most promising directions for reaching higher levels of artificial intelligence.
Among the other achievements, building computers that understand speech represents a crucial leap towards intelligent machines. Despite the great efforts of the past decades, however, a natural and robust human-machine speech interaction still appears to be out of reach, especially when users interact with a distant microphone in noisy and reverberant environments. The latter disturbances severely hamper the intelligibility of a speech signal, making Distant Speech Recognition (DSR) one of the major open challenges in the field.
This thesis addresses the latter scenario and proposes some novel techniques, architectures, and algorithms to improve the robustness of distant-talking acoustic models. We first elaborate on methodologies for realistic data contamination, with a particular emphasis on DNN training with simulated data. We then investigate on approaches for better exploiting speech contexts, proposing some original methodologies for both feed-forward and recurrent neural networks. Lastly, inspired by the idea that cooperation across different DNNs could be the key for counteracting the harmful effects of noise and reverberation, we propose a novel deep learning paradigm called “network of deep neural networks”.
The analysis of the original concepts were based on extensive experimental validations conducted on both real and simulated data, considering different corpora, microphone configurations, environments, noisy conditions, and ASR tasks.
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Liu, Yulan. "Distant speech recognition of natural spontaneous multi-party conversations." Thesis, University of Sheffield, 2017. http://etheses.whiterose.ac.uk/17691/.
Distant speech recognition (DSR) has gained wide interest recently. While deep networks keep improving ASR overall, the performance gap remains between using close-talking recordings and distant recordings. Therefore the work in this thesis aims at providing some insights for further improvement of DSR performance. The investigation starts with collecting the first multi-microphone and multi-media corpus of natural spontaneous multi-party conversations in native English with the speaker location tracked, i.e. the Sheffield Wargame Corpus (SWC). The state-of-the-art recognition systems with the acoustic models trained standalone and adapted both show word error rates (WERs) above 40% on headset recordings and above 70% on distant recordings. A comparison between SWC and AMI corpus suggests a few unique properties in the real natural spontaneous conversations, e.g. the very short utterances and the emotional speech. Further experimental analysis based on simulated data and real data quantifies the impact of such influence factors on DSR performance, and illustrates the complex interaction among multiple factors which makes the treatment of each influence factor much more difficult. The reverberation factor is studied further. It is shown that the reverberation effect on speech features could be accurately modelled with a temporal convolution in the complex spectrogram domain. Based on that a polynomial reverberation score is proposed to measure the distortion level of short utterances. Compared to existing reverberation metrics like C50, it avoids a rigid early-late-reverberation partition without compromising the performance on ranking the reverberation level of recording environments and channels. Furthermore, the existing reverberation measurement is signal independent thus unable to accurately estimate the reverberation distortion level in short recordings. Inspired by the phonetic analysis on the reverberation distortion via self-masking and overlap-masking, a novel partition of reverberation distortion into the intra-phone smearing and the inter-phone smearing is proposed, so that the reverberation distortion level is first estimated on each part and then combined.
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Mahdian, Toroghi Rahil [Verfasser], and Dietrich [Akademischer Betreuer] Klakow. "Blind speech separation in distant speech recognition front-end processing / Rahil Mahdian Toroghi ; Betreuer: Dietrich Klakow." Saarbrücken : Saarländische Universitäts- und Landesbibliothek, 2016. http://d-nb.info/1119802229/34.
Eldridge, Larry D. "A distant heritage : the growth of free speech in early America /." New York, NY [u.a.] : New York Univ. Press, 1994. http://www.gbv.de/dms/spk/sbb/recht/toc/277767857.pdf.
Titus, Andrew Richard. "A study of adaptive enhancement methods for improved distant speech recognition." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/119706.
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018. This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Cataloged from student-submitted PDF version of thesis. Includes bibliographical references (pages 65-68). Automatic speech recognition systems trained on speech data recorded by microphones placed close to the speaker tend to perform poorly on speech recorded by microphones placed farther away from the speaker due to reverberation effects and background noise. I designed and implemented a variety of machine learning models to improve distant speech recognition performance by adaptively enhancing incoming speech to appear as if it was recorded in a close-talking environment, regardless of whether it was originally recorded in a close-talking or distant environment. These were evaluated by passing the enhanced speech to acoustic models trained on only close-talking speech and comparing error rates to those achieved without speech enhancement. Experiments conducted on the AMI, TIMIT and TED-LIUM datasets indicate that decreases in error rate on distant speech of up to 33% relative can be achieved by these with only minor increases (1% relative) on clean speech. by Andrew Richard Titus. M. Eng.
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Guerrero, Flores Cristina Maritza. "Information Fusion Approaches for Distant Speech Recognition in a Multi-microphone Setting." Doctoral thesis, Università degli studi di Trento, 2016. https://hdl.handle.net/11572/368955.
It is a well known fact that high quality Automatic Speech Recognition is still difficult to guarantee under conditions in which the speaker is distant from the microphone due to the distortions caused by acoustic phenomena, such as noise and reverberation. Among the different research directions pursued around this problem, the adoption of multi-channel approaches is of great interest to the community given the potential of taking advantage of information diversity.
In this thesis we elaborate on approaches that exploit different instances of a sound source, captured by various largely spaced microphones, in order to extract a Distant Speech Recognition hypothesis. Two original solutions are presented, based on information fusion approaches at different levels of the recognition system, one at front-end stage and one at post-decoding stage, namely for the problems of channel selection (CS) and hypothesis combination. First, a new CS framework is proposed. Cepstral distance (CD), which is effectively applied in other acoustic processing fields, is the basis of the CS method developed. Experimental results confirmed the advantages of a CD-based selection schema under different scenarios. The second contribution concerns the combination of information extracted from the individual decoding processes performed over the multiple captured signals. It is shown how temporal cues can be identified in the hypothesis space, and be beneficial for the elaboration of a multi-microphone confusion network, from which the final speech transcription is derived. The proposed methods are applicable in a setting equipped with synchronized distributed microphones, independently of the proximity between the sensors. Analysis of the novel concepts were performed over synthetic and real-captured data. Both approaches achieved positive results at the different assessment tasks they were exposed to.
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Guerrero, Flores Cristina Maritza. "Information Fusion Approaches for Distant Speech Recognition in a Multi-microphone Setting." Doctoral thesis, University of Trento, 2016. http://eprints-phd.biblio.unitn.it/1853/1/160830_cguerrero_phd-thesis.pdf.
It is a well known fact that high quality Automatic Speech Recognition is still difficult to guarantee under conditions in which the speaker is distant from the microphone due to the distortions caused by acoustic phenomena, such as noise and reverberation. Among the different research directions pursued around this problem, the adoption of multi-channel approaches is of great interest to the community given the potential of taking advantage of information diversity.
In this thesis we elaborate on approaches that exploit different instances of a sound source, captured by various largely spaced microphones, in order to extract a Distant Speech Recognition hypothesis. Two original solutions are presented, based on information fusion approaches at different levels of the recognition system, one at front-end stage and one at post-decoding stage, namely for the problems of channel selection (CS) and hypothesis combination. First, a new CS framework is proposed. Cepstral distance (CD), which is effectively applied in other acoustic processing fields, is the basis of the CS method developed. Experimental results confirmed the advantages of a CD-based selection schema under different scenarios. The second contribution concerns the combination of information extracted from the individual decoding processes performed over the multiple captured signals. It is shown how temporal cues can be identified in the hypothesis space, and be beneficial for the elaboration of a multi-microphone confusion network, from which the final speech transcription is derived. The proposed methods are applicable in a setting equipped with synchronized distributed microphones, independently of the proximity between the sensors. Analysis of the novel concepts were performed over synthetic and real-captured data. Both approaches achieved positive results at the different assessment tasks they were exposed to.
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Swietojanski, Paweł. "Learning representations for speech recognition using artificial neural networks." Thesis, University of Edinburgh, 2016. http://hdl.handle.net/1842/22835.
Learning representations is a central challenge in machine learning. For speech recognition, we are interested in learning robust representations that are stable across different acoustic environments, recording equipment and irrelevant inter– and intra– speaker variabilities. This thesis is concerned with representation learning for acoustic model adaptation to speakers and environments, construction of acoustic models in low-resource settings, and learning representations from multiple acoustic channels. The investigations are primarily focused on the hybrid approach to acoustic modelling based on hidden Markov models and artificial neural networks (ANN). The first contribution concerns acoustic model adaptation. This comprises two new adaptation transforms operating in ANN parameters space. Both operate at the level of activation functions and treat a trained ANN acoustic model as a canonical set of fixed-basis functions, from which one can later derive variants tailored to the specific distribution present in adaptation data. The first technique, termed Learning Hidden Unit Contributions (LHUC), depends on learning distribution-dependent linear combination coefficients for hidden units. This technique is then extended to altering groups of hidden units with parametric and differentiable pooling operators. We found the proposed adaptation techniques pose many desirable properties: they are relatively low-dimensional, do not overfit and can work in both a supervised and an unsupervised manner. For LHUC we also present extensions to speaker adaptive training and environment factorisation. On average, depending on the characteristics of the test set, 5-25% relative word error rate (WERR) reductions are obtained in an unsupervised two-pass adaptation setting. The second contribution concerns building acoustic models in low-resource data scenarios. In particular, we are concerned with insufficient amounts of transcribed acoustic material for estimating acoustic models in the target language – thus assuming resources like lexicons or texts to estimate language models are available. First we proposed an ANN with a structured output layer which models both context–dependent and context–independent speech units, with the context-independent predictions used at runtime to aid the prediction of context-dependent states. We also propose to perform multi-task adaptation with a structured output layer. We obtain consistent WERR reductions up to 6.4% in low-resource speaker-independent acoustic modelling. Adapting those models in a multi-task manner with LHUC decreases WERRs by an additional 13.6%, compared to 12.7% for non multi-task LHUC. We then demonstrate that one can build better acoustic models with unsupervised multi– and cross– lingual initialisation and find that pre-training is a largely language-independent. Up to 14.4% WERR reductions are observed, depending on the amount of the available transcribed acoustic data in the target language. The third contribution concerns building acoustic models from multi-channel acoustic data. For this purpose we investigate various ways of integrating and learning multi-channel representations. In particular, we investigate channel concatenation and the applicability of convolutional layers for this purpose. We propose a multi-channel convolutional layer with cross-channel pooling, which can be seen as a data-driven non-parametric auditory attention mechanism. We find that for unconstrained microphone arrays, our approach is able to match the performance of the comparable models trained on beamform-enhanced signals.
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KITAOKA, Norihide, Seiichi NAKAGAWA, and Longbiao WANG. "Robust Speech Recognition by Combining Short-Term and Long-Term Spectrum Based Position-Dependent CMN with Conventional CMN." Institute of Electronics, Information and Communication Engineers, 2008. http://hdl.handle.net/2237/14966.
Speed, Charles S. Call of a distant drum: The Speeds, the Crittendons, and the New Land. Arlington, VA (2000 South Eads St., Arlington 22202): C.S. Speed, 1986.
Hunt, M. J. Distance measures for speech recognition =: Les distances spectrales pour la reconnaissance de la parole. Ottawa: National Research Council Canada, 1989.
DIMED 86 (1986 Algarve, Portugal). DIMED 86: Discurso dos media e ensino a distância = discours des médias et enseignement à distance = media speech and distance teaching : actas do colóquio, Algarve 10-15 março de 1986. Lisboa: Ministério da Educação e Cultura, Instituto Português de Ensino a Distância, 1986.
Kendall, Henry W. "Speech to the United Nations Intergovernmental Negotiating Committee." In A Distant Light, 189–91. New York, NY: Springer New York, 2000. http://dx.doi.org/10.1007/978-1-4419-8507-1_17.
Zając, Marcin, and Adam Przepiórkowski. "Distant Supervision Learning of DBPedia Relations." In Text, Speech, and Dialogue, 193–200. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40585-3_25.
Haderlein, Tino, Elmar Nöth, Wolfgang Herbordt, Walter Kellermann, and Heinrich Niemann. "Using Artificially Reverberated Training Data in Distant-Talking ASR." In Text, Speech and Dialogue, 226–33. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11551874_29.
Ji, Mikyong, Sungtak Kim, and Hoirin Kim. "Composite Decision by Bayesian Inference in Distant-Talking Speech Recognition." In Text, Speech and Dialogue, 463–70. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11846406_58.
Radeck-Arneth, Stephan, Benjamin Milde, Arvid Lange, Evandro Gouvêa, Stefan Radomski, Max Mühlhäuser, and Chris Biemann. "Open Source German Distant Speech Recognition: Corpus and Acoustic Model." In Text, Speech, and Dialogue, 480–88. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24033-6_54.
Vacher, Michel, Benjamin Lecouteux, and François Portet. "On Distant Speech Recognition for Home Automation." In Smart Health, 161–88. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16226-3_7.
Golubev, Anton, and Natalia Loukachevitch. "Use of Augmentation and Distant Supervision for Sentiment Analysis in Russian." In Text, Speech, and Dialogue, 184–96. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-83527-9_16.
Renals, Steve, and Pawel Swietojanski. "Distant Speech Recognition Experiments Using the AMI Corpus." In New Era for Robust Speech Recognition, 355–68. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-64680-0_16.
Astapov, Sergei, Gleb Svirskiy, Aleksandr Lavrentyev, Tatyana Prisyach, Dmitriy Popov, Dmitriy Ubskiy, and Vladimir Kabarov. "Acoustic Event Mixing to Multichannel AMI Data for Distant Speech Recognition and Acoustic Event Classification Benchmarking." In Speech and Computer, 31–42. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-26061-3_4.
Lin, Q., C. Che, D. S. Yak, L. Jin, B. deVries, J. Pearson, and J. Flanagan. "Robust distant-talking speech recognition." In 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings. IEEE, 1996. http://dx.doi.org/10.1109/icassp.1996.540280.
Thotappa, Deepak, and S. R. Mahadeva Prasanna. "Analysis of Foreground and Distant speech." In TENCON 2015 - 2015 IEEE Region 10 Conference. IEEE, 2015. http://dx.doi.org/10.1109/tencon.2015.7372808.
Jin, Qin, Runxin Li, Qian Yang, Kornel Laskowski, and Tanja Schultz. "Speaker identification with distant microphone speech." In 2010 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2010. http://dx.doi.org/10.1109/icassp.2010.5495590.
Morales-Cordovilla, Juan A., Hannes Pessentheiner, Martin Hagmüller, and Gernot Kubin. "Room localization for distant speech recognition." In Interspeech 2014. ISCA: ISCA, 2014. http://dx.doi.org/10.21437/interspeech.2014-238.
McDonough, John, and Matthias Wolfel. "Distant Speech Recognition: Bridging the Gaps." In 2008 Hands-Free Speech Communication and Microphone Arrays (HSCMA 2008). IEEE, 2008. http://dx.doi.org/10.1109/hscma.2008.4538699.
Renals, Steve, and Pawel Swietojanski. "Neural networks for distant speech recognition." In 2014 4th Joint Workshop on Hands-free Speech Communication and Microphone Arrays (HSCMA). IEEE, 2014. http://dx.doi.org/10.1109/hscma.2014.6843274.
Zhao, Tuo, Yunxin Zhao, Shaojun Wang, and Mei Han. "UNet++-Based Multi-Channel Speech Dereverberation and Distant Speech Recognition." In 2021 12th International Symposium on Chinese Spoken Language Processing (ISCSLP). IEEE, 2021. http://dx.doi.org/10.1109/iscslp49672.2021.9362064.
Zwyssig, Erich, Mike Lincoln, and Steve Renals. "A digital microphone array for distant speech recognition." In 2010 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2010. http://dx.doi.org/10.1109/icassp.2010.5495040.
Zilberman, Mark. Methods to Test the “Dimming Effect” Produced by a Decrease in the Number of Photons Received from Receding Light Sources. Intellectual Archive, June 2021. http://dx.doi.org/10.32370/ia_2021_06_22.
The hypothetical “Dimming effect” describes the change of the number of photons arriving from a receding light source per unit of time. In non-relativistic systems,the "Dimming effect" occurs due to the fact that as light sources move away, the distance between the emitter and the receiver constantly increases, and the photons always take longer to reach the receiver. This reduces the number of photons received per time unit compared to the number of emitted photons per time unit. Negligible for speeds incomparable with the speed of light c, the "Dimming effect" can be very significant for speeds above 0.1c. “Dimming effect” can possibly be tested in a physics labor-atory using a moving light source (or mirror) and photon counters located in the travel direction and in opposite direction. It can possibly also be tested utilizing the orbital movement of the Earth around the Sun. If confirmed, “Dimming effect” would allow astronomers to adjust values of the "Standard Candles", which are critical in cosmological models. Absence of “Dimming effect” will mean that the number of photons arriving per time unit does not depend on the relative speed of light source and observer,which is not so apparent
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Stastny, Petr, Robert Roczniok, Daniel Cleather, Martin Musalek, Dominik Novak, and Michal Vagner. Straight speed and acceleration optimal distances and reference values. A systematic review, and meta-analyses. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, May 2022. http://dx.doi.org/10.37766/inplasy2022.5.0010.
Review question / Objective: To summarize the sprint reference acceleration and speed values for different sprint distances and suggest optimal unification of ice-hockey straight sprint testing. Eligibility criteria: The title and abstract screening was done by two researchers (PS and RR) who selected a set of articles for full text screening, where the inclusion criteria were: 1) male or female ice-hockey players; 2) any cross-sectional or intervention study; 3) tests of ice-hockey sprinting over any distance or any battery of conditioning tests that included straight-line sprints; and, 4) results reported straight-line sprint distance, speed, time, or acceleration. In the case of disagreement between the evaluating authors, the final decision was made by a third author (MV).The full text screening exclusion criteria were: 1) if the article was not in English; 2) the testing did not include straight-line sprinting; 3) the reported values did not include data distribution; 4) the study reported only maximum speed without skating time or average speed; 5) the end of the sprint was defined by the point the player stopped sprinting; 6) the measurement was made with a stopwatch; and, 7) the study had high bias estimation. The maximum speed test was not included due to the uncertain velocity conditions at beginning of testing distance. The bias estimation was performed using the JBI (Joanna Briggs Institute) Critical Appraisal Checklist for Analytical Cross Sectional Studies (supplementary material 1).
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Harris and Vaze. PR-185-0351-R05 Welding for Small to Medium Diameter Gas Pipelines - Real-Time Quality Monitoring. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), April 2006. http://dx.doi.org/10.55274/r0011071.
A literature review was conducted to establish and highlight the state of the art for real-time quality monitoring (RTQM). Work in this task advanced the state of the art in terms of achieving constant welding power input at constant contact tip-to-workpiece distance (CTWD), thus achieving constant welding heat input at constant welding travel speed. This was coupled with high speed on-board DAQ to achieve RTQM, in these terms.
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Bernau, Jeremiah. The Changing Bonneville Salt Flats. Utah Geological Survey, January 2024. http://dx.doi.org/10.34191/pi-106.
The Bonneville Salt Flats in western Utah are renowned worldwide for hosting land-speed racing events. They are easily accessible from Interstate 80 and are managed by the Bureau of Land Management. This distinct landscape spans approximately 5 miles wide by 12 miles long, featuring a smooth, hard salt crust that draws thousands of visitors each year.
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Nascimento, Lucas, Augusto Boening, Abílio Galli, Janaine Polese, and Louise Ada. Mechanically-assisted walking training for improving walking speed, walking distance and social participation after stroke: a systematic review protocol. INPLASY - International Platform of Registered Systematic Review Protocols, March 2020. http://dx.doi.org/10.37766/inplasy2020.3.0007.
Rahai, Hamid, and Assma Begum. Numerical Investigations of Transient Wind Shear from Passing Vehicles Near a Road Structure (Part I: Unsteady Reynolds-Averaged Navier-Stokes Simulations). Mineta Transportation Institute, January 2021. http://dx.doi.org/10.31979/mti.2020.1933.
In this research, the authors performed unsteady numerical simulations of a moving Ahmed body under a freeway overpass at different distances from the bridge columns in order to evaluate transient wind shear and the wind load on these columns. Results have shown that when the vehicle is at 0.75W distance from the bridge columns, an unsteady wind speed of up to 24 m/s is observed at the columns with a pressure coefficient difference of 0.9. Here W is the width of the vehicle. These results indicate with an appropriate system for harnessing these wind energy potentials, significant renewable electric power could be generated with zero carbon footprint.
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Heron, Neil, and Sam McCormick. The Physical Demands of Wheelchair Tennis - a Systematic Review. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, March 2023. http://dx.doi.org/10.37766/inplasy2023.3.0060.
Review question / Objective: Our aim was to describe and synthesise the physical demands of wheelchair tennis. We reviewed the data across different playing surfaces, performance levels and sex of tennis players. Eligibility criteria: Studies had to meet the criteria below to be included in the review:i. The paper reported on participants playing singles or doubles wheelchair tennis matches (all ages, performance levels, quad or open category and court surfaces).ii. The data collected was related to the duration of play (e.g. length of match, effective playing time), on-court movement characteristics (e.g. distance covered, moving speed, accelerations), stroke characteristics (e.g. first serve %, count, frequency) or physiological response to match play (e.g. heart rate, oxygen uptake, energy expenditure) of wheelchair tennis.
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Idris, Iffat. Preventing Atrocities in Conflict and Non-conflict Settings. Institute of Development Studies, August 2022. http://dx.doi.org/10.19088/k4d.2022.137.
Atrocity prevention refers to activities to prevent atrocity crimes against civilians. These include genocide, war crimes, crimes against humanity and ethnic cleansing, and can take place in both conflict and non-conflict settings. This points to the need to prioritise and implement atrocity prevention specifically, and not just as part of conflict prevention efforts. Atrocity prevention interventions are broadly of two types: operational (short-term responses) and structural (addressing underlying causes/drivers). These encompass a wide range of approaches including: acting locally (with local actors taking the lead in prevention activities); tackling hate speech, and promoting an independent and strong media; documenting human rights violations, and prosecuting and punishing those responsible (establishing rule of law). The international community should prioritise atrocity prevention, but work in a united manner, take a comprehensive approach, and give the lead to local actors. Atrocity crimes generally develop in a process over time, and risk factors can be identified; these traits make atrocity prevention possible. This rapid review looks at the concept of atrocity prevention, how it is distinct from conflict prevention, the different approaches taken to atrocity prevention, and the lessons learned from these. The review draws on a mixture of academic and grey literature, in particular reports produced by international development organisations such as the United Nations (UN) and USAID. The literature was largely gender-blind (with the exception of conflict-related sexual violence) and disability-blind.
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Kubica, Stefan, Tobias Peuschke-Bischof, Belinda Müller, and Robin Avci. Fahrmanöver für Geradeausfahrt. Technische Hochschule Wildau, 2019. http://dx.doi.org/10.15771/1264.
This repository contains various short driving scenarios of our vehicle fleet in 1:14 scale. These are part of our digital learning factory "Wildauer Maschinen Werke" (www.th-wildau.de/wmw) and are used for the development of autonomous driving functions. The data is recorded in the form of Rosbags in the underlying robotic operating system (www.ros.org) and can be played in their own ROS server instances, whereby the recorded journeys of the vehicles can be simulated and used for their own developments. The scenarios contain short driving scenarios with so-called ROS topics. This includes engine and steering control and frontal distance measurements of an ultrasonic sensor. Videos are also provided for each scenario for a better overview. It is the 1st generation. Towards the end of 2020, data sets will follow that additionally contain camera data, GPS coordinates and speed as well as laser scanner data.
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Tao, Yang, Amos Mizrach, Victor Alchanatis, Nachshon Shamir, and Tom Porter. Automated imaging broiler chicksexing for gender-specific and efficient production. United States Department of Agriculture, December 2014. http://dx.doi.org/10.32747/2014.7594391.bard.
Extending the previous two years of research results (Mizarch, et al, 2012, Tao, 2011, 2012), the third year’s efforts in both Maryland and Israel were directed towards the engineering of the system. The activities included the robust chick handling and its conveyor system development, optical system improvement, online dynamic motion imaging of chicks, multi-image sequence optimal feather extraction and detection, and pattern recognition. Mechanical System Engineering The third model of the mechanical chick handling system with high-speed imaging system was built as shown in Fig. 1. This system has the improved chick holding cups and motion mechanisms that enable chicks to open wings through the view section. The mechanical system has achieved the speed of 4 chicks per second which exceeds the design specs of 3 chicks per second. In the center of the conveyor, a high-speed camera with UV sensitive optical system, shown in Fig.2, was installed that captures chick images at multiple frames (45 images and system selectable) when the chick passing through the view area. Through intensive discussions and efforts, the PIs of Maryland and ARO have created the protocol of joint hardware and software that uses sequential images of chick in its fall motion to capture opening wings and extract the optimal opening positions. This approached enables the reliable feather feature extraction in dynamic motion and pattern recognition. Improving of Chick Wing Deployment The mechanical system for chick conveying and especially the section that cause chicks to deploy their wings wide open under the fast video camera and the UV light was investigated along the third study year. As a natural behavior, chicks tend to deploy their wings as a mean of balancing their body when a sudden change in the vertical movement was applied. In the latest two years, this was achieved by causing the chicks to move in a free fall, in the earth gravity (g) along short vertical distance. The chicks have always tended to deploy their wing but not always in wide horizontal open situation. Such position is requested in order to get successful image under the video camera. Besides, the cells with checks bumped suddenly at the end of the free falling path. That caused the chicks legs to collapse inside the cells and the image of wing become bluer. For improving the movement and preventing the chick legs from collapsing, a slowing down mechanism was design and tested. This was done by installing of plastic block, that was printed in a predesign variable slope (Fig. 3) at the end of the path of falling cells (Fig.4). The cells are moving down in variable velocity according the block slope and achieve zero velocity at the end of the path. The slop was design in a way that the deacceleration become 0.8g instead the free fall gravity (g) without presence of the block. The tests showed better deployment and wider chick's wing opening as well as better balance along the movement. Design of additional sizes of block slops is under investigation. Slops that create accelerations of 0.7g, 0.9g, and variable accelerations are designed for improving movement path and images.
