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Статті в журналах з теми "Multiple-Model coding"
Qing, Linbo, Xiaohai He, Xianfeng Ou, and Rui Lv. "Distributed Video Coding Based on Multiple-source Correlation Model." Applied Mathematics & Information Sciences 7, no. 4 (July 1, 2013): 1609–14. http://dx.doi.org/10.12785/amis/070447.
Повний текст джерелаZhou, Yugang, and Wai-Yip Chan. "E-model based comparison of multiple description coding and layered coding in packet networks." European Transactions on Telecommunications 18, no. 7 (2007): 661–68. http://dx.doi.org/10.1002/ett.1168.
Повний текст джерелаZhang, Jinlin, Jun Shao, Yun Ling, Min Ji, Guiyi Wei, and Bishan Ying. "Efficient multiple sources network coding signature in the standard model." Concurrency and Computation: Practice and Experience 27, no. 10 (July 25, 2014): 2616–36. http://dx.doi.org/10.1002/cpe.3322.
Повний текст джерелаWANG, NING, and LIZHONG PENG. "BALANCED MULTIPLE DESCRIPTION SUBBAND CODING." International Journal of Wavelets, Multiresolution and Information Processing 09, no. 04 (July 2011): 571–86. http://dx.doi.org/10.1142/s0219691311004225.
Повний текст джерелаBai, Yang, Xuan Guang, and Raymond W. Yeung. "Multiple Linear-Combination Security Network Coding." Entropy 25, no. 8 (July 28, 2023): 1135. http://dx.doi.org/10.3390/e25081135.
Повний текст джерелаLi, Shizheng, and Aditya Ramamoorthy. "Multiple-Source Slepian-Wolf Coding Under a Linear Equation Correlation Model." IEEE Transactions on Communications 60, no. 9 (September 2012): 2402–7. http://dx.doi.org/10.1109/tcomm.2012.070912.110062.
Повний текст джерелаSwanson, H. Lee. "Verbal coding deficits in learning-disabled readers: A multiple stage model." Educational Psychology Review 1, no. 3 (September 1989): 235–77. http://dx.doi.org/10.1007/bf01320136.
Повний текст джерелаGreenberg, Jane, Shaun Trujillo, and Ketan Mayer-Patel. "YouTube: Applying FRBR and Exploring the Multiple Description Coding Compression Model." Cataloging & Classification Quarterly 50, no. 5-7 (June 2012): 742–62. http://dx.doi.org/10.1080/01639374.2012.681273.
Повний текст джерелаWang, Cui Yan, Wei Sheng Du, and Jun Wang. "Optimization Algorithm Study for Multiple-Constrained and Multiple-Objective Job-Shop Tool Dynamic Distribution." Applied Mechanics and Materials 551 (May 2014): 612–16. http://dx.doi.org/10.4028/www.scientific.net/amm.551.612.
Повний текст джерелаLee, Bum-Jik, Young-Hoon Joo, and Jin-Bae Park. "A DNA Coding-Based Interacting Multiple Model Method for Tracking a Maneuvering Target." Journal of Korean Institute of Intelligent Systems 12, no. 6 (December 1, 2002): 497–502. http://dx.doi.org/10.5391/jkiis.2002.12.6.497.
Повний текст джерелаДисертації з теми "Multiple-Model coding"
Presvôts, Corentin. "Multiple-Model Coding Scheme for Electrical Signal Compression." Electronic Thesis or Diss., université Paris-Saclay, 2025. http://www.theses.fr/2025UPASG007.
Повний текст джерелаThe integration of renewable energy sources into the electrical grid introduces more complex dynamics, requiring more efficient and low-latency control. This control demands the transmission of large volumes of data from substations to higher-level control centers. To address network bandwidth constraints, it is essential to develop efficient compression techniques tailored to the specific characteristics of electrical signals. Currently, Phasor Measurement Units (PMUs) are the most commonly used devices for acquiring and compressing these signals. However, PMU cannot accurately represent fast transients. In the first part of this thesis, a compression method using neural networks, based on Variational Autoencoders (VAEs), is described. Initially developed for image compression, this method is adapted in this work to the compression of sampled electrical signals. The principle relies on optimizing the transformation step by minimizing a Rate-Distortion (RD) trade-off over a training set of electrical signals. This method enables the optimization of the entire compression pipeline by accounting for quantization and entropy coding, thus providing better average performance compared to classical non-trainable methods, such as the Discrete Cosine Transform (DCT) or the discrete wavelet transform Discrete Wavelet Transform (DWT). The second part describes a proposed multi-model coding scheme for the compression of sampled electrical signals. To reduce latency, the Multiple Model Compression (MMC) approach operates on windows of approximately one electrical period (on the order of glspl{pmu} latency), thus containing a limited number of samples. In the first compression stage, several parametric signal models (sinusoidal, polynomial, and predictive models) are compared to obtain a coarse representation of the electrical signal. In the second stage, several transform coding techniques are used to compress the reconstruction residual from the first stage. The transformations used include the DCT, DWT, and the previously mentioned VAEs. The model parameters are quantized, and the bit budget allocation between the two stages is optimized based on a target rate. However, imposing a rate constraint introduces variations in the reconstruction quality of the sampled signals, which may not meet the strict quality requirements imposed by end users. The third part of this thesis aims to determine the minimum bit budget required to satisfy a maximum distortion constraint. The additional degree of freedom provided by the choice of the total bit budget increases the complexity of the MMC approach compared to its fixed-rate variant and may be incompatible with real-time constraints. Two distinct methods are proposed for model selection and bit allocation between the two stages. The first method relies on an exhaustive search to determine the number of bits to allocate to both compression stages. The second method uses a golden-section search. The computational cost of these two approaches is further reduced by a preliminary estimation of the best model and the optimal bit allocation between the two stages to meet the distortion constraint. This estimation is based on RD models
Li, Xiaohuan. "Multiple Global Affine Motion Models Used in Video Coding." Diss., Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/14631.
Повний текст джерелаAbouseif, Akram. "Emerging DSP techniques for multi-core fiber transmission systems." Electronic Thesis or Diss., Institut polytechnique de Paris, 2020. http://www.theses.fr/2020IPPAT013.
Повний текст джерелаOptical communication systems have seen several phases in the last decades. It is predictable that the optical systems as we know will reach the non-linear capacity limits. At the moment, the space is the last degree of freedom to be implemented in order to keep delivering the upcoming capacity demands for the next years. Therefore, intensive researches are conducted to explore all the aspects concerning the deployment of the space-division multiplexing (SDM) system. Several impairments impact the SDM systems as a result from the interaction of the spatial channels which degrades the system performance. In this thesis, we focus on the multi-core fibers (MCFs) as the most promising approach to be the first representative of the SDM system. We present different digital and optical solutions to mitigate the non-unitary effect known as the core dependent loss (CDL). The first part is dedicated to study the performance of the MCF transmission taking into account the propagating impairments that impact the MCF systems. We propose a channel model that helps to identify the MCFs system. The second part is devoted to optical technique to enhance the transmission performance with an optimal solution. After, we introduced digital techniques for further enhancement, the Zero Forcing pre-compensation and the space-time coding for further CDL mitigation. All the simulation results are validated analytically by deriving the error probability upper bounds
Wen, Shan-Tsun, and 溫善淳. "Auto-Regressive Model Enhanced Multiple Description Coding." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/74788150929379530898.
Повний текст джерела國立交通大學
網路工程研究所
100
Multiple description video coding (MDC) [1] is one of popular solutions to reduce the detrimental effects caused by transmission over error-prone networks. In this thesis, an auto-regressive model enhanced MDC is proposed. In general MDC architecture, redundancy rate and error resilience performance are important criterion for assessment. Auto-regressive model adopted in our proposal aims at reducing the redundancy rate while keeping the error resilience performance in our proposal. The proposed MDC model comprises two symmetric descriptions. One description is composed of even frames in h.264 standard and odd residual frames; while the other is omposed of odd frames and even residual frames. Both even and odd residual frames use the prediction frames generated by auto-regressive model. The experiments show that it achieves better coding efficiency and error resilience than descriptions which residual frames are predicted from interpolated frames in packet loss networks.
Wang, Ching-Yen, and 王敬嚴. "Using Auto-Regressive Model with Multiple Training Window Sizes in Multiple Description Coding." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/48546022850410142896.
Повний текст джерела國立交通大學
資訊科學與工程研究所
101
Since network video streaming have become popular in recent years, error resilient technique is more important. Multiple description video coding is one of well-known error resilient methods to cope with the network erroneous transmission in various networks environments. In conventional auto-regressive model, fixed training window size is adopted. In this thesis, we design a multiple description coding which adopts an auto-regressive model with multiple training window sizes to enhance the error resilience. In our MDC structure, we encode a video stream into two descriptions; one description contains all odd frames and the other contains all even frames. Both are encoded according to H.264/AVC standard. In the decoder side, we recover missing frames by using auto-regressive model with selected training window sizes. According to the experimental results, the proposed method outperforms other methods in both objective and subjective quality.
Varshneya, Virendra K. "Distributed Coding For Wireless Sensor Networks." Thesis, 2005. https://etd.iisc.ac.in/handle/2005/1409.
Повний текст джерелаVarshneya, Virendra K. "Distributed Coding For Wireless Sensor Networks." Thesis, 2005. http://etd.iisc.ernet.in/handle/2005/1409.
Повний текст джерелаBhavani, Shankar M. R. "Design Of Linear Precoded MIMO Communication Systems." Thesis, 2007. https://etd.iisc.ac.in/handle/2005/558.
Повний текст джерелаBhavani, Shankar M. R. "Design Of Linear Precoded MIMO Communication Systems." Thesis, 2007. http://hdl.handle.net/2005/558.
Повний текст джерела"Differential modulation and non-coherent detection in wireless relay networks." Thesis, 2014. http://hdl.handle.net/10388/ETD-2014-01-1399.
Повний текст джерелаЧастини книг з теми "Multiple-Model coding"
Shao, Jun, Jinlin Zhang, Yun Ling, Min Ji, Guiyi Wei, and Bishan Ying. "Multiple Sources Network Coding Signature in the Standard Model." In Internet and Distributed Computing Systems, 195–208. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41428-2_16.
Повний текст джерелаAhlswede, Rudolf. "Coding for the Multiple-Access Channel: The Combinatorial Model." In Foundations in Signal Processing, Communications and Networking, 113–232. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-53139-7_4.
Повний текст джерелаCoyle, Yvette. "Chapter 14. Setting up a coding scheme for the analysis of the dynamics of children’s engagement with written corrective feedback." In Research Methods in the Study of L2 Writing Processes, 292–314. Amsterdam: John Benjamins Publishing Company, 2023. http://dx.doi.org/10.1075/rmal.5.14coy.
Повний текст джерелаLessa, Felipe, Daniele Martins Neto, Kátia Guimarães, Marcelo Brigido, and Maria Emilia Walter. "Regene: Automatic Construction of a Multiple Component Dirichlet Mixture Priors Covariance Model to Identify Non-coding RNA." In Bioinformatics Research and Applications, 380–91. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21260-4_36.
Повний текст джерелаMenges, Uta, Jonas Hielscher, Annette Kluge, and M. Angela Sasse. "Work in Progress – Brick by Brick: Using a Structured Building Blocks Method to Engage Participants and Collect IT Security Insights." In Lecture Notes in Computer Science, 134–45. Cham: Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-83072-3_8.
Повний текст джерелаShipp, Stewart, and Karl Friston. "Predictive Coding." In The Cerebral Cortex and Thalamus, edited by Alessandra Angelucci, 436–45. Oxford University PressNew York, 2023. http://dx.doi.org/10.1093/med/9780197676158.003.0041.
Повний текст джерелаCormier, Ben. "Testing the Partisan Model." In How Governments Borrow, 57–79. Oxford University PressOxford, 2024. http://dx.doi.org/10.1093/oso/9780198882732.003.0003.
Повний текст джерела"YouTube: Applying FRBR and Exploring the Multiple Description Coding Compression Model." In The FRBR Family of Conceptual Models, 398–418. Routledge, 2014. http://dx.doi.org/10.4324/9781315829661-28.
Повний текст джерелаHayashi, Sachiko. "Multiple layers of gene-expression regulatory mechanisms during fermentation and respiration." In New Advances in Saccharomyces [Working Title]. IntechOpen, 2023. http://dx.doi.org/10.5772/intechopen.1003912.
Повний текст джерелаFrança, Reinaldo Padilha, Yuzo Iano, Ana Carolina Borges Monteiro, and Rangel Arthur. "Improvement for Channels With Multipath Fading (MF) Through the Methodology CBEDE." In Advances in Systems Analysis, Software Engineering, and High Performance Computing, 25–43. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-1152-7.ch002.
Повний текст джерелаТези доповідей конференцій з теми "Multiple-Model coding"
Satti, Shahid M., Nikos Deligiannis, Adrian Munteanu, Peter Schelkens, and Jan Cornelis. "A model-based analysis of scalable Multiple Description Coding." In 2011 17th International Conference on Digital Signal Processing (DSP). IEEE, 2011. http://dx.doi.org/10.1109/icdsp.2011.6004891.
Повний текст джерелаAlsabbagh, J. R., and V. V. Raghavan. "A model for multiple-query processing based upon strong factoring." In International Conference on Information Technology: Coding and Computing, 2004. Proceedings. ITCC 2004. IEEE, 2004. http://dx.doi.org/10.1109/itcc.2004.1286511.
Повний текст джерелаSagetong, P., and A. Ortega. "Analytical model-based bit allocation for wavelet coding with applications to multiple description coding and region of interest coding." In IEEE International Conference on Multimedia and Expo, 2001. ICME 2001. IEEE, 2001. http://dx.doi.org/10.1109/icme.2001.1237645.
Повний текст джерелаZaveri, M. A., S. N. Merchant, and U. B. Desai. "Arbitrary trajectories tracking using multiple model based particle filtering in infrared image sequence." In International Conference on Information Technology: Coding and Computing, 2004. Proceedings. ITCC 2004. IEEE, 2004. http://dx.doi.org/10.1109/itcc.2004.1286530.
Повний текст джерелаShahnawazuddin, S., and Rohit Sinha. "A low complexity model adaptation approach involving sparse coding over multiple dictionaries." In Interspeech 2014. ISCA: ISCA, 2014. http://dx.doi.org/10.21437/interspeech.2014-498.
Повний текст джерелаKabatiansky, Grigory, and Elena Egorova. "Adversarial multiple access channels and a new model of multimedia fingerprinting coding." In 2020 IEEE Conference on Communications and Network Security (CNS). IEEE, 2020. http://dx.doi.org/10.1109/cns48642.2020.9162248.
Повний текст джерелаKulatunga, Harini, and V. Kadirkamanathan. "Space-Time Block Coding: Joint Detection and Channel Estimation using Multiple Model Theory." In 2006 IEEE 7th Workshop on Signal Processing Advances in Wireless Communications. IEEE, 2006. http://dx.doi.org/10.1109/spawc.2006.346407.
Повний текст джерелаLi, Xiaohuan, Joel R. Jackson, Aggelos K. Katsaggelos, and Russel M. Merserau. "Multiple global affine motion model for H.264 video coding with low bit rate." In Electronic Imaging 2005, edited by Amir Said and John G. Apostolopoulos. SPIE, 2005. http://dx.doi.org/10.1117/12.587328.
Повний текст джерелаChoi, Minkyu, and Jun Tani. "Predictive coding for dynamic vision: Development of functional hierarchy in a multiple spatio-temporal scales RNN model." In 2017 International Joint Conference on Neural Networks (IJCNN). IEEE, 2017. http://dx.doi.org/10.1109/ijcnn.2017.7965915.
Повний текст джерелаMillidge, Beren, Tommaso Salvatori, Yuhang Song, Rafal Bogacz, and Thomas Lukasiewicz. "Predictive Coding: Towards a Future of Deep Learning beyond Backpropagation?" In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/774.
Повний текст джерелаЗвіти організацій з теми "Multiple-Model coding"
DiGrande, Laura, Sue Pedrazzani, Elizabeth Kinyara, Melanie Hymes, Shawn Karns, Donna Rhodes, and Alanna Moshfegh. Field Interviewer– Administered Dietary Recalls in Participants’ Homes: A Feasibility Study Using the US Department of Agriculture’s Automated Multiple-Pass Method. RTI Press, May 2021. http://dx.doi.org/10.3768/rtipress.2021.mr.0045.2105.
Повний текст джерелаEshed, Yuval, and John Bowman. Harnessing Fine Scale Tuning of Endogenous Plant Regulatory Processes for Manipulation of Organ Growth. United States Department of Agriculture, 2005. http://dx.doi.org/10.32747/2005.7696519.bard.
Повний текст джерела