Literatura científica selecionada sobre o tema "Translation de signal à signal"
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Artigos de revistas sobre o assunto "Translation de signal à signal"
Finidori, J., L. Rizzolo, A. Gonzalez, G. Kreibich, M. Adesnik e D. D. Sabatini. "The influenza hemagglutinin insertion signal is not cleaved and does not halt translocation when presented to the endoplasmic reticulum membrane as part of a translocating polypeptide." Journal of Cell Biology 104, n.º 6 (1 de junho de 1987): 1705–14. http://dx.doi.org/10.1083/jcb.104.6.1705.
Texto completo da fonteRong, Chao, Dingfan Zhang, Yuwen Cao e Zhengbin Li. "Analyze the Difference Between Rotational and Translational Motions Produced by High-speed Train". Journal of Physics: Conference Series 2651, n.º 1 (1 de dezembro de 2023): 012141. http://dx.doi.org/10.1088/1742-6596/2651/1/012141.
Texto completo da fontePah, Nemuel D., e Dinesh Kant Kumar. "Thresholding Wavelet Networks for Signal Classification". International Journal of Wavelets, Multiresolution and Information Processing 01, n.º 03 (setembro de 2003): 243–61. http://dx.doi.org/10.1142/s0219691303000220.
Texto completo da fonteChen, Zhuo. "Signal Recognition for English Speech Translation Based on Improved Wavelet Denoising Method". Advances in Mathematical Physics 2021 (18 de setembro de 2021): 1–9. http://dx.doi.org/10.1155/2021/6811192.
Texto completo da fonteYang, Ying, e Yusen Wei. "RANDOM INTERPOLATION AVERAGE FOR ECG SIGNAL DENOISING USING MULTIPLE WAVELET BASES". Biomedical Engineering: Applications, Basis and Communications 25, n.º 04 (agosto de 2013): 1350042. http://dx.doi.org/10.4015/s1016237213500427.
Texto completo da fonteLipp, J., N. Flint, M. T. Haeuptle e B. Dobberstein. "Structural requirements for membrane assembly of proteins spanning the membrane several times." Journal of Cell Biology 109, n.º 5 (1 de novembro de 1989): 2013–22. http://dx.doi.org/10.1083/jcb.109.5.2013.
Texto completo da fonteShome, Debaditya, Pritam Sarkar e Ali Etemad. "Region-Disentangled Diffusion Model for High-Fidelity PPG-to-ECG Translation". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 13 (24 de março de 2024): 15009–19. http://dx.doi.org/10.1609/aaai.v38i13.29422.
Texto completo da fonteWild, Klemens, Matthias M. M. Becker, Georg Kempf e Irmgard Sinning. "Structure, dynamics and interactions of large SRP variants". Biological Chemistry 401, n.º 1 (18 de dezembro de 2019): 63–80. http://dx.doi.org/10.1515/hsz-2019-0282.
Texto completo da fonteWarr, Paul A., e Alan M. Potter. "A Reduced-Complexity Mixer Linearization Scheme". Research Letters in Communications 2009 (2009): 1–4. http://dx.doi.org/10.1155/2009/541084.
Texto completo da fonteRobinson, A., O. M. R. Westwood e B. M. Austen. "Interactions of signal peptides with signal-recognition particle". Biochemical Journal 266, n.º 1 (15 de fevereiro de 1990): 149–56. http://dx.doi.org/10.1042/bj2660149.
Texto completo da fonteTeses / dissertações sobre o assunto "Translation de signal à signal"
Ponnala, Lalit. "Analysis of Genetic Translation using Signal Processing". NCSU, 2007. http://www.lib.ncsu.edu/theses/available/etd-02072007-174200/.
Texto completo da fonteGirault, Benjamin. "Signal Processing on Graphs - Contributions to an Emerging Field". Thesis, Lyon, École normale supérieure, 2015. http://www.theses.fr/2015ENSL1046/document.
Texto completo da fonteThis dissertation introduces in its first part the field of signal processing on graphs. We start by reminding the required elements from linear algebra and spectral graph theory. Then, we define signal processing on graphs and give intuitions on its strengths and weaknesses compared to classical signal processing. In the second part, we introduce our contributions to the field. Chapter 4 aims at the study of structural properties of graphs using classical signal processing through a transformation from graphs to time series. Doing so, we take advantage of a unified method of semi-supervised learning on graphs dedicated to classification to obtain a smooth time series. Finally, we show that we can recognize in our method a smoothing operator on graph signals. Chapter 5 introduces a new translation operator on graphs defined by analogy to the classical time shift operator and verifying the key property of isometry. Our operator is compared to the two operators of the literature and its action is empirically described on several graphs. Chapter 6 describes the use of the operator above to define stationary graph signals. After giving a spectral characterization of these graph signals, we give a method to study and test stationarity on real graph signals. The closing chapter shows the strength of the matlab toolbox developed and used during the course of this PhD
Messaoud, Safa. "Translating Discrete Time SIMULINK to SIGNAL". Thesis, Virginia Tech, 2014. http://hdl.handle.net/10919/49299.
Texto completo da fonteMaster of Science
Mittermayr, Lukas Verfasser], e Dario Michael [Akademischer Betreuer] [Leister. "Identification of factors involved in the translation : dependant signal transduction process / Lukas Mittermayr. Betreuer: Dario Leister". München : Universitätsbibliothek der Ludwig-Maximilians-Universität, 2013. http://d-nb.info/1070464910/34.
Texto completo da fonteDe, Laurentiis Evelina Ines. "Kinetic analyses on two translational GTPases : LepA and EF-Tu". Thesis, Lethbridge, Alta. : University of Lethbridge, Dept. of Chemistry and Biochemistry, 2013. http://hdl.handle.net/10133/3450.
Texto completo da fontexiii, 177 leaves : col. ill. ; 29 cm
Jacquet, Gottfried. "Hybrid physics-based/data-based seismic ground motion generator of a site". Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPAST035.
Texto completo da fonteAccurately estimating the seismic response following an earthquake can save lives. However, limited computational resources and poorly characterized and unknown variability in geology and seismotectonic context pose significant challenges for simulations at the scale of a city or region. This thesis proposes a new approach com- bining adversarial learning methods and physics-based simulations to overcome these limitations, based on the SeismoALICE framework (F. GATTI and D. CLOUTEAU: "Towards blending Physics-Based numerical simulations and seismic databases using Generative Adversarial Network," CMAME 2020). Because of the random fluctuations in the mechanical properties of the geological medium, numerical simulations can only give results for low frequencies (LF) down to 5 or even 10 Hz. The design frequency for civil engineering structures and equipment, on the other hand, reaches 40 Hz. This thesis aims to simulate seismic signals with a higher frequency range [0 - 30 Hz] using knowledge of low-frequency signals and a database of recorded signals. To this end, we are developing an encoder and decoder adapted to seismic signals using a Conformer variant of attention techniques to capture the long-duration correlations present in accelerograms. The discriminator, which ensures that simulated signals resemble recorded signals, has been the subject of extensive development, enabling the encoder and decoder to be optimized using a min-max technique at the heart of adversarialmachine learning methods. To force signal recon- struction, we adapt Focal Frequency Loss (FFL) and Hyper-Spherical Loss (HSL), which are more efficient for this data type, to time series. We then complement the LF signals up to 30 Hz by ex- ploring different generation cases, one-to-one map- ping, and one-to-many mapping to assess the plausibility of the reconstructions in the database. Five methods were developed: Signal-to-Signal Translation, SeismoALICE with shared latent space, SeismoALICE with factorized latent space, BicycleGAN for time series, and Multi-Modal Signal Translation. Their performance was evaluated using Kristeková's Goodness-of-Fit. By manipulating the hidden variables, we proved that it is possible to divide the information into two groups of variables with Gaussian distributions, one for low frequencies and the other for high frequencies. This interpretability made it possible to manipulate the latent space and control the one-to-many mapping. The models, trained on 128,000 seismic signals from the Stanford Earthquake Database (STEAD), demonstrated their performance, with prediction qualities ranging from good to excellent. Finally, their effectiveness was demonstrated by application to the 2019 Le Teil earthquake (in the Ardèche region of Auvergne-Rhone-Alpes, France). This work paves the way for more accurate and efficient prediction of seismic signals by seamlessly integrating physics-based knowledge and machine learning
Kerins, Michael John, Ajay Amar Vashisht, Benjamin Xi-Tong Liang, Spencer Jordan Duckworth, Brandon John Praslicka, James Akira Wohlschlegel e Aikseng Ooi. "Fumarate Mediates a Chronic Proliferative Signal in Fumarate Hydratase-Inactivated Cancer Cells by Increasing Transcription and Translation of Ferritin Genes". AMER SOC MICROBIOLOGY, 2017. http://hdl.handle.net/10150/624216.
Texto completo da fonteHamirally, Sofia. "Mechanistic studies of the translational readthrough signal of Moloney murine leukemia virus". Thesis, University of Cambridge, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.619933.
Texto completo da fonteColberg, Clara Ottilie Freifrau Loeffelholz von. "Etudes au microscope électronique du transport des protéines durant la traduction chez E. Coli, et de la terminaison de la traduction chez l'homme". Thesis, Grenoble, 2013. http://www.theses.fr/2013GRENV038/document.
Texto completo da fonteThe signal recognition particle (SRP) and its receptor (FtsY in Escherichia coli) mediate co-translational protein targeting by delivering ribosome nascent chain complexes (RNCs) to the target membrane. Recognition of an RNC cargo by SRP is dependent on an N-terminal signal sequence. Binding of FtsY to the RNC-SRP complex leads to several conformational changes of SRP and FtsY during the targeting cycle: first, an “early” GTP-independent state is adopted which is stabilized by the RNC, subsequently a “closed” GTP- dependent conformation is formed which can activate itself to hydrolyze GTP (the “activated” state). Faithful completion of all three steps leads to release of the cargo from SRP-FtsY and hand over of the RNC to the translocation pore.It has been shown for E. coli that cargos can be rejected from the SRP pathway during all targeting steps. In the first project, our interest concentrates on ribosomes translating the EspP signal sequence (RNCEspP). In vivo, EspP is a post-translationally targeted protein, but RNCEspP has been shown to be bound by SRP and FtsY leading to a non-productive “early”-like RNCEspP-SRP-FtsY complex. Using single particle cryo-electron microscopy (EM), we analysed the structural basis for the rejection of RNCEspP by SRP and FtsY. Comparison of our RNCEspP-SRP-FtsY cryo-EM structure to other available cryo-EM structures of co-translational targeting complexes containing the correct cargo RNCFtsQ unravelled differences in the SRP-FtsY structure between a correct cargo and an incorrect cargo. Two major differences between the targeting complexes containing the cargos RNCFtsQ and RNCEspP were observed: first, the Ffh M-domain was attached to ribosomal RNA helix 59 of RNCEspP, while it was detached from this site in the case of RNCFtsQ. It could be that such an ordered M-domain is hampering the release of the signal sequence which is required for successful completion of targeting. Second, the Ffh-FtsY NG-domain arrangement was flexible in the complex with RNCEspP in comparison to RNCFtsQ indicating that the "early"-like complex formed on RNCEspP is less stable. Biochemical data using fluorescence resonance energy transfer corroborated these results, showing that FtsY is bound with lower affinity in the RNCEspP “early” complex and that the rearrangement to the “closed” conformation is less efficient. Further biochemical analysis of EspP signal sequence variants showed that mainly the N-terminal extension of the EspP signal sequence is responsible for its rejection from the SRP pathway
Ma, Chon Teng. "Biopotential readout front-end circuits using frequency-translation filtering techniques". Thesis, University of Macau, 2010. http://umaclib3.umac.mo/record=b2182904.
Texto completo da fonteLivros sobre o assunto "Translation de signal à signal"
Wimmer, Natasha. Some kind of beautiful signal. [San Francisco, Calif.]: Center for the Art of Translation, 2010.
Encontre o texto completo da fonteChristie, Agatha. Rasskazhi,kak ty zhivesh ; Prikli︠u︡chenii︠a︡ rozhdestvenskogo pudinga ; Signal bedstvii︠a︡: Roman, rasskazy. Novosibirsk: Germes, 1995.
Encontre o texto completo da fonteMoyal, Ami. Phonetic Search Methods for Large Speech Databases. New York, NY: Springer New York, 2013.
Encontre o texto completo da fonte(Firm), Lotus, ed. Signal seminar guide: Lotus Signal. San Mateo, Calif. (1900 S. Norfolk St., San Mateo 94403): Lotus, 1985.
Encontre o texto completo da fonteEcole d'été de physique théorique (Les Houches, Haute-Savoie, France) (45th 1985). Traitement du signal =: Signal processing. Amsterdam: North-Holland, 1987.
Encontre o texto completo da fonteDeFelice, Cynthia C. Signal. New York: Scholastic, 2011.
Encontre o texto completo da fonteBöhme, Klaus-Richard. Signal. Stockholm: Bokförlaget DN, 2005.
Encontre o texto completo da fonteDimoski, Slave Ǵorǵo. Signal. Skopje: Tri, 2002.
Encontre o texto completo da fonteDeFelice, Cynthia C. Signal. New York: Farrar, Straus and Giroux, 2009.
Encontre o texto completo da fonteBruce, Eugene N. Biomedical signal processing and signal modeling. New York: Wiley, 2001.
Encontre o texto completo da fonteCapítulos de livros sobre o assunto "Translation de signal à signal"
Marks, Friedrich, Ursula Klingmüller e Karin Müller-Decker. "Signals Controlling mRNA Translation". In Cellular Signal Processing, 329–58. Second edition. | New York, NY: Garland Science, 2017.: Garland Science, 2017. http://dx.doi.org/10.4324/9781315165479-9.
Texto completo da fontePnueli, A., O. Shtrichman e M. Siegel. "Translation Validation: From SIGNAL to C". In Lecture Notes in Computer Science, 231–55. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48092-7_11.
Texto completo da fonteAliannejadi, Mohammad, Shahram Khadivi, Saeed Shiry Ghidary e Mohammad Hadi Bokaei. "Discriminative Spoken Language Understanding Using Statistical Machine Translation Alignment Models". In Artificial Intelligence and Signal Processing, 194–202. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10849-0_20.
Texto completo da fonteNoormohammadi, Neda, Zahra Rahimi e Shahram Khadivi. "Improving Reordering Models with Phrase Number Feature for Statistical Machine Translation". In Artificial Intelligence and Signal Processing, 227–33. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10849-0_23.
Texto completo da fonteMahsuli, Mohammad Mahdi, e Shahram Khadivi. "Word-Level Confidence Estimation for Statistical Machine Translation Using IBM-1 Model". In Artificial Intelligence and Signal Processing, 241–49. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10849-0_25.
Texto completo da fonteFuchs, Eckart. "The Translation Initiation Signal in E.Coli and its Control". In Genetic Engineering, 15–35. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-4707-5_2.
Texto completo da fonteBo, Tang, e Sun Qiang. "Analysis on Suppression of Echo Signal of Target Body and Translation in Micro-Doppler Signal Processing". In Lecture Notes in Electrical Engineering, 205–11. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-0187-6_23.
Texto completo da fonteCrespo, José. "Space Connectivity and Translation-Invariance". In Mathematical Morphology and its Applications to Image and Signal Processing, 119–26. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4613-0469-2_14.
Texto completo da fonteMadankar, Mangala, Manoj Chandak e Nekita Chavhan. "Information Retrieval System Based on Query Translation Approach for Cross-Languages". In Advances in Automation, Signal Processing, Instrumentation, and Control, 1261–69. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8221-9_118.
Texto completo da fonteNguyen-Vo, Thang H., Duc Truong, Long H. B. Nguyen e Dien Dinh. "Exploring Subword Segmentation Methods in English-Vietnamese Neural Machine Translation". In Advances in Intelligent Information Hiding and Multimedia Signal Processing, 324–30. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6757-9_41.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Translation de signal à signal"
Starck, J. L., e A. Bijaoui. "Multi-Resolution Deconvolution". In Signal Recovery and Synthesis. Washington, D.C.: Optica Publishing Group, 1992. http://dx.doi.org/10.1364/srs.1992.tud4.
Texto completo da fonteShokouhmand, Arash, e Negar Tavassolian. "Fetal Movement Cancellation in Abdominal Electrocardiogram Recordings Using Signal-to-Signal Translation". In 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 2022. http://dx.doi.org/10.1109/embc48229.2022.9871826.
Texto completo da fonteCONSTANTINIDES, AG, e TE CURTIS. "HIGH EFFICIENCY WAVE TRANSLATION FILTERS FOR SONAR BAND SELECTION". In Sonar Signal Processing 1989. Institute of Acoustics, 2024. http://dx.doi.org/10.25144/21674.
Texto completo da fonteDas, Aditya Kaustav, Manabhanjan Pradhan, Amiya Kumar Dash, Chittaranjan Pradhan e Himansu Das. "A Constructive Machine Translation System for English to Odia Translation". In 2018 International Conference on Communication and Signal Processing (ICCSP). IEEE, 2018. http://dx.doi.org/10.1109/iccsp.2018.8524268.
Texto completo da fonteBilevich, L., e L. Yaroslavsky. "Fast DCT-based algorithms for signal convolution and translation". In 2009 16th International Conference on Digital Signal Processing (DSP). IEEE, 2009. http://dx.doi.org/10.1109/icdsp.2009.5201263.
Texto completo da fontePeralta, Julio C., Thierry Gautier, Loic Besnard e Paul Le Guernic. "LTSs for translation validation of (multi-clocked) SIGNAL specifications". In 2010 8th IEEE/ACM International Conference on Formal Methods and Models for Codesign (MEMOCODE 2010). IEEE, 2010. http://dx.doi.org/10.1109/memcod.2010.5558632.
Texto completo da fonteGirault, Benjamin. "Stationary graph signals using an isometric graph translation". In 2015 23rd European Signal Processing Conference (EUSIPCO). IEEE, 2015. http://dx.doi.org/10.1109/eusipco.2015.7362637.
Texto completo da fonteArmanious, Karim, Chenming Jiang, Sherif Abdulatif, Thomas Kustner, Sergios Gatidis e Bin Yang. "Unsupervised Medical Image Translation Using Cycle-MedGAN". In 2019 27th European Signal Processing Conference (EUSIPCO). IEEE, 2019. http://dx.doi.org/10.23919/eusipco.2019.8902799.
Texto completo da fonteWu, Youzheng, Xinhu Hu e Chiori Hori. "Translating TED speeches by recurrent neural network based translation model". In ICASSP 2014 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2014. http://dx.doi.org/10.1109/icassp.2014.6854977.
Texto completo da fonteBirnie, Lachlan, Zamir Ben-Hur, Vladimir Tourbabin, Thushara Abhayapala e Prasanga Samarasinghe. "Bilateral-Ambisonic Reproduction by Soundfield Translation". In 2022 International Workshop on Acoustic Signal Enhancement (IWAENC). IEEE, 2022. http://dx.doi.org/10.1109/iwaenc53105.2022.9914780.
Texto completo da fonteRelatórios de organizações sobre o assunto "Translation de signal à signal"
Chamovitz, Daniel, e Albrecht Von Arnim. Translational regulation and light signal transduction in plants: the link between eIF3 and the COP9 signalosome. United States Department of Agriculture, novembro de 2006. http://dx.doi.org/10.32747/2006.7696515.bard.
Texto completo da fonteChamovitz, Daniel, e Xing-Wang Deng. Morphogenesis and Light Signal Transduction in Plants: The p27 Subunit of the COP9-Complex. United States Department of Agriculture, 1997. http://dx.doi.org/10.32747/1997.7580666.bard.
Texto completo da fonteBarash, Itamar, e Robert Rhoads. Translational Mechanisms Governing Milk Protein Levels and Composition. United States Department of Agriculture, 2006. http://dx.doi.org/10.32747/2006.7696526.bard.
Texto completo da fonteEaston, Jr., R. Signal processing. Office of Scientific and Technical Information (OSTI), janeiro de 1990. http://dx.doi.org/10.2172/5071979.
Texto completo da fonteMiller, Jr, e Willard. Signal Processing. Fort Belvoir, VA: Defense Technical Information Center, fevereiro de 1989. http://dx.doi.org/10.21236/ada206662.
Texto completo da fonteCromwell, R. Signal Processing Studies Program Optical Signal Amplification. Volume 2. Fort Belvoir, VA: Defense Technical Information Center, setembro de 1987. http://dx.doi.org/10.21236/ada188054.
Texto completo da fonteRoehrig, H., e M. Browne. Signal Processing Studies Program Optical Signal Amplification. Volume 1. Fort Belvoir, VA: Defense Technical Information Center, setembro de 1987. http://dx.doi.org/10.21236/ada188055.
Texto completo da fonteBasu, Sankar. Multidimensional Signal Processing. Fort Belvoir, VA: Defense Technical Information Center, junho de 1988. http://dx.doi.org/10.21236/ada200954.
Texto completo da fonteThomas, J. B., e K. Steiglitz. Digital Signal Processing. Fort Belvoir, VA: Defense Technical Information Center, dezembro de 1988. http://dx.doi.org/10.21236/ada203744.
Texto completo da fonteArmenta, Mikaela, Laura Epifanovskaya, Joshua Letchford, Kiran Lakkaraju, Jonathan Whetzel, Bethany Goldblum e Jake Tibbetts. SIGNAL Game Manual. Office of Scientific and Technical Information (OSTI), julho de 2020. http://dx.doi.org/10.2172/1643226.
Texto completo da fonte