Auswahl der wissenschaftlichen Literatur zum Thema „Translation de signal à signal“
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Zeitschriftenartikel zum Thema "Translation de signal à signal"
Finidori, J., L. Rizzolo, A. Gonzalez, G. Kreibich, M. Adesnik und 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, Nr. 6 (01.06.1987): 1705–14. http://dx.doi.org/10.1083/jcb.104.6.1705.
Der volle Inhalt der QuelleRong, Chao, Dingfan Zhang, Yuwen Cao und Zhengbin Li. „Analyze the Difference Between Rotational and Translational Motions Produced by High-speed Train“. Journal of Physics: Conference Series 2651, Nr. 1 (01.12.2023): 012141. http://dx.doi.org/10.1088/1742-6596/2651/1/012141.
Der volle Inhalt der QuellePah, Nemuel D., und Dinesh Kant Kumar. „Thresholding Wavelet Networks for Signal Classification“. International Journal of Wavelets, Multiresolution and Information Processing 01, Nr. 03 (September 2003): 243–61. http://dx.doi.org/10.1142/s0219691303000220.
Der volle Inhalt der QuelleChen, Zhuo. „Signal Recognition for English Speech Translation Based on Improved Wavelet Denoising Method“. Advances in Mathematical Physics 2021 (18.09.2021): 1–9. http://dx.doi.org/10.1155/2021/6811192.
Der volle Inhalt der QuelleYang, Ying, und Yusen Wei. „RANDOM INTERPOLATION AVERAGE FOR ECG SIGNAL DENOISING USING MULTIPLE WAVELET BASES“. Biomedical Engineering: Applications, Basis and Communications 25, Nr. 04 (August 2013): 1350042. http://dx.doi.org/10.4015/s1016237213500427.
Der volle Inhalt der QuelleLipp, J., N. Flint, M. T. Haeuptle und B. Dobberstein. „Structural requirements for membrane assembly of proteins spanning the membrane several times.“ Journal of Cell Biology 109, Nr. 5 (01.11.1989): 2013–22. http://dx.doi.org/10.1083/jcb.109.5.2013.
Der volle Inhalt der QuelleShome, Debaditya, Pritam Sarkar und Ali Etemad. „Region-Disentangled Diffusion Model for High-Fidelity PPG-to-ECG Translation“. Proceedings of the AAAI Conference on Artificial Intelligence 38, Nr. 13 (24.03.2024): 15009–19. http://dx.doi.org/10.1609/aaai.v38i13.29422.
Der volle Inhalt der QuelleWild, Klemens, Matthias M. M. Becker, Georg Kempf und Irmgard Sinning. „Structure, dynamics and interactions of large SRP variants“. Biological Chemistry 401, Nr. 1 (18.12.2019): 63–80. http://dx.doi.org/10.1515/hsz-2019-0282.
Der volle Inhalt der QuelleWarr, Paul A., und 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.
Der volle Inhalt der QuelleRobinson, A., O. M. R. Westwood und B. M. Austen. „Interactions of signal peptides with signal-recognition particle“. Biochemical Journal 266, Nr. 1 (15.02.1990): 149–56. http://dx.doi.org/10.1042/bj2660149.
Der volle Inhalt der QuelleDissertationen zum Thema "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/.
Der volle Inhalt der QuelleGirault, Benjamin. „Signal Processing on Graphs - Contributions to an Emerging Field“. Thesis, Lyon, École normale supérieure, 2015. http://www.theses.fr/2015ENSL1046/document.
Der volle Inhalt der QuelleThis 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.
Der volle Inhalt der QuelleMaster of Science
Mittermayr, Lukas Verfasser], und 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.
Der volle Inhalt der QuelleDe, 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.
Der volle Inhalt der Quellexiii, 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.
Der volle Inhalt der QuelleAccurately 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 und 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.
Der volle Inhalt der QuelleHamirally, 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.
Der volle Inhalt der QuelleColberg, 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.
Der volle Inhalt der QuelleThe 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.
Der volle Inhalt der QuelleBücher zum Thema "Translation de signal à signal"
Wimmer, Natasha. Some kind of beautiful signal. [San Francisco, Calif.]: Center for the Art of Translation, 2010.
Den vollen Inhalt der Quelle findenChristie, Agatha. Rasskazhi,kak ty zhivesh ; Prikli︠u︡chenii︠a︡ rozhdestvenskogo pudinga ; Signal bedstvii︠a︡: Roman, rasskazy. Novosibirsk: Germes, 1995.
Den vollen Inhalt der Quelle findenMoyal, Ami. Phonetic Search Methods for Large Speech Databases. New York, NY: Springer New York, 2013.
Den vollen Inhalt der Quelle finden(Firm), Lotus, Hrsg. Signal seminar guide: Lotus Signal. San Mateo, Calif. (1900 S. Norfolk St., San Mateo 94403): Lotus, 1985.
Den vollen Inhalt der Quelle findenEcole d'été de physique théorique (Les Houches, Haute-Savoie, France) (45th 1985). Traitement du signal =: Signal processing. Amsterdam: North-Holland, 1987.
Den vollen Inhalt der Quelle findenDeFelice, Cynthia C. Signal. New York: Scholastic, 2011.
Den vollen Inhalt der Quelle findenBöhme, Klaus-Richard. Signal. Stockholm: Bokförlaget DN, 2005.
Den vollen Inhalt der Quelle findenDimoski, Slave Ǵorǵo. Signal. Skopje: Tri, 2002.
Den vollen Inhalt der Quelle findenDeFelice, Cynthia C. Signal. New York: Farrar, Straus and Giroux, 2009.
Den vollen Inhalt der Quelle findenBruce, Eugene N. Biomedical signal processing and signal modeling. New York: Wiley, 2001.
Den vollen Inhalt der Quelle findenBuchteile zum Thema "Translation de signal à signal"
Marks, Friedrich, Ursula Klingmüller und 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.
Der volle Inhalt der QuellePnueli, A., O. Shtrichman und 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.
Der volle Inhalt der QuelleAliannejadi, Mohammad, Shahram Khadivi, Saeed Shiry Ghidary und 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.
Der volle Inhalt der QuelleNoormohammadi, Neda, Zahra Rahimi und 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.
Der volle Inhalt der QuelleMahsuli, Mohammad Mahdi, und 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.
Der volle Inhalt der QuelleFuchs, 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.
Der volle Inhalt der QuelleBo, Tang, und 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.
Der volle Inhalt der QuelleCrespo, 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.
Der volle Inhalt der QuelleMadankar, Mangala, Manoj Chandak und 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.
Der volle Inhalt der QuelleNguyen-Vo, Thang H., Duc Truong, Long H. B. Nguyen und 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.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Translation de signal à signal"
Starck, J. L., und 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.
Der volle Inhalt der QuelleShokouhmand, Arash, und 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.
Der volle Inhalt der QuelleCONSTANTINIDES, AG, und 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.
Der volle Inhalt der QuelleDas, Aditya Kaustav, Manabhanjan Pradhan, Amiya Kumar Dash, Chittaranjan Pradhan und 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.
Der volle Inhalt der QuelleBilevich, L., und 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.
Der volle Inhalt der QuellePeralta, Julio C., Thierry Gautier, Loic Besnard und 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.
Der volle Inhalt der QuelleGirault, 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.
Der volle Inhalt der QuelleArmanious, Karim, Chenming Jiang, Sherif Abdulatif, Thomas Kustner, Sergios Gatidis und 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.
Der volle Inhalt der QuelleWu, Youzheng, Xinhu Hu und 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.
Der volle Inhalt der QuelleBirnie, Lachlan, Zamir Ben-Hur, Vladimir Tourbabin, Thushara Abhayapala und 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.
Der volle Inhalt der QuelleBerichte der Organisationen zum Thema "Translation de signal à signal"
Chamovitz, Daniel, und Albrecht Von Arnim. Translational regulation and light signal transduction in plants: the link between eIF3 and the COP9 signalosome. United States Department of Agriculture, November 2006. http://dx.doi.org/10.32747/2006.7696515.bard.
Der volle Inhalt der QuelleChamovitz, Daniel, und 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.
Der volle Inhalt der QuelleBarash, Itamar, und 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.
Der volle Inhalt der QuelleEaston, Jr., R. Signal processing. Office of Scientific and Technical Information (OSTI), Januar 1990. http://dx.doi.org/10.2172/5071979.
Der volle Inhalt der QuelleMiller, Jr, und Willard. Signal Processing. Fort Belvoir, VA: Defense Technical Information Center, Februar 1989. http://dx.doi.org/10.21236/ada206662.
Der volle Inhalt der QuelleCromwell, R. Signal Processing Studies Program Optical Signal Amplification. Volume 2. Fort Belvoir, VA: Defense Technical Information Center, September 1987. http://dx.doi.org/10.21236/ada188054.
Der volle Inhalt der QuelleRoehrig, H., und M. Browne. Signal Processing Studies Program Optical Signal Amplification. Volume 1. Fort Belvoir, VA: Defense Technical Information Center, September 1987. http://dx.doi.org/10.21236/ada188055.
Der volle Inhalt der QuelleBasu, Sankar. Multidimensional Signal Processing. Fort Belvoir, VA: Defense Technical Information Center, Juni 1988. http://dx.doi.org/10.21236/ada200954.
Der volle Inhalt der QuelleThomas, J. B., und K. Steiglitz. Digital Signal Processing. Fort Belvoir, VA: Defense Technical Information Center, Dezember 1988. http://dx.doi.org/10.21236/ada203744.
Der volle Inhalt der QuelleArmenta, Mikaela, Laura Epifanovskaya, Joshua Letchford, Kiran Lakkaraju, Jonathan Whetzel, Bethany Goldblum und Jake Tibbetts. SIGNAL Game Manual. Office of Scientific and Technical Information (OSTI), Juli 2020. http://dx.doi.org/10.2172/1643226.
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