Relevant bibliographies by topics / Quantization errors

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Quantization errors'

Author: Grafiati
Published: 4 June 2021
Last updated: 19 February 2022

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Journal articles on the topic "Quantization errors"

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Zheng, Dong Xi. "The Realization and Comparison of Directly Fitting Method and Edge Extraction Method Used in Amending the Quantization Errors." Advanced Materials Research 546-547 (July 2012): 537–41. http://dx.doi.org/10.4028/www.scientific.net/amr.546-547.537.

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The quantization error is one of errors in 3D measuring, the method of amending the quantization errors includes directly fitting method and edge extraction method. Analyzed the source of quantization errors and the principle to amend the quantization errors, and analyzed the simulation test of directly fitting method and edge extraction method, and compared them with each other.
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Dai, Xinyan, Xiao Yan, Kelvin K. W. Ng, Jiu Liu, and James Cheng. "Norm-Explicit Quantization: Improving Vector Quantization for Maximum Inner Product Search." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (April 3, 2020): 51–58. http://dx.doi.org/10.1609/aaai.v34i01.5333.

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Vector quantization (VQ) techniques are widely used in similarity search for data compression, computation acceleration and etc. Originally designed for Euclidean distance, existing VQ techniques (e.g., PQ, AQ) explicitly or implicitly minimize the quantization error. In this paper, we present a new angle to analyze the quantization error, which decomposes the quantization error into norm error and direction error. We show that quantization errors in norm have much higher influence on inner products than quantization errors in direction, and small quantization error does not necessarily lead to good performance in maximum inner product search (MIPS). Based on this observation, we propose norm-explicit quantization (NEQ) — a general paradigm that improves existing VQ techniques for MIPS. NEQ quantizes the norms of items in a dataset explicitly to reduce errors in norm, which is crucial for MIPS. For the direction vectors, NEQ can simply reuse an existing VQ technique to quantize them without modification. We conducted extensive experiments on a variety of datasets and parameter configurations. The experimental results show that NEQ improves the performance of various VQ techniques for MIPS, including PQ, OPQ, RQ and AQ.
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Qin, Li Juan. "Robustness Problem Argumentation from Image Quantization Errors in Vision Location." Advanced Materials Research 225-226 (April 2011): 1332–35. http://dx.doi.org/10.4028/www.scientific.net/amr.225-226.1332.

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In our vision location system, error is inevitable. Image quantization errors play an important role in computer vision field. Quantization errors are the primary sources that affect the precision of pose estimation and they are inherent and unavoidable. It is important to analysis on the effect of this error on compute process. In this paper, Robustness problem argumentation in vision location is presented in detail. Then we introduce image quantization error. Robustness mathematical model for vision location is set up at last.
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Ti-Chiun Chang and J. P. Allebach. "Quantization of accumulated diffused errors in error diffusion." IEEE Transactions on Image Processing 14, no. 12 (December 2005): 1960–76. http://dx.doi.org/10.1109/tip.2005.859372.

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Vallabha, Gautam K., and Betty Tuller. "Quantization errors in formant estimation." Journal of the Acoustical Society of America 107, no. 5 (May 2000): 2907. http://dx.doi.org/10.1121/1.428820.

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Kushner, H. B., M. Meisner, and A. V. Levy. "Almost uniformity of quantization errors." IEEE Transactions on Instrumentation and Measurement 40, no. 4 (1991): 682–87. http://dx.doi.org/10.1109/19.85334.

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Stenbakken, G. N., Dong Liu, J. A. Starzyk, and B. C. Waltrip. "Nonrandom quantization errors in timebases." IEEE Transactions on Instrumentation and Measurement 50, no. 4 (2001): 888–92. http://dx.doi.org/10.1109/19.948294.

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Xu, De Hong, Huan Xin Peng, and Bin Liu. "Non-Uniform Probabilistically Quantized Distributed Consensus Applied on Sensors Network." Applied Mechanics and Materials 577 (July 2014): 921–25. http://dx.doi.org/10.4028/www.scientific.net/amm.577.921.

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In order to improve the accuracy of distributed consensus under digital communication, in the paper, assuming the initial states of nodes following uniform distribution, a non-uniform quantization scheme based on probabilistic quantization is proposed, and the entire data range is divided based on µ-law non-uniform quantization scheme. The quantization step-size near the average of initial states is smaller, and the corresponding quantization errors are smaller. Base on the proposed quantization scheme, a non-uniform probabilistically quantized distributed consensus algorithm is proposed. The performance and the mean square errors of the non-uniform probabilistically quantized distributed consensus algorithm is analyzed, by analyses and simulations, the results show the non-uniform probabilistically quantized distributed consensus can reach a consensus, and the mean square error is far smaller than that of probabilistically quantized distributed consensus.
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Pawlus, Paweł, Rafał Reizer, and Dominik Czach. "The effect of vertical resolution on measurement errors of machined surfaces topography." Mechanik 91, no. 11 (November 12, 2018): 988–91. http://dx.doi.org/10.17814/mechanik.2018.11.177.

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The effect of the quantization error on values of surface topography parameters was examined. Surface topography was measured using an optical profilometer of 0.01 nm vertical resolution. Twenty isotropic and anisotropic, one- and two-process, random and deterministic surfaces were objects of investigations. The vertical resolution was changed using TalyMap software. Tendencies of changes of three surfaces due to quantization errors were analyzed in details. Parameters of the highest and the smallest sensitivity on errors were selected.
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Koeck, P. J. B. "Quantization errors in averaged digitized data." Signal Processing 81, no. 2 (February 2001): 345–56. http://dx.doi.org/10.1016/s0165-1684(00)00212-7.

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Dissertations / Theses on the topic "Quantization errors"

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Tangboondouangjit, Aram. "Sigma-Delta quantization number theoretic aspects of refining quantization error /." College Park, Md. : University of Maryland, 2006. http://hdl.handle.net/1903/3793.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2006.
Thesis research directed by: Mathematics. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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Pötzelberger, Klaus. "The Consistency ot the Empirical Quantization Error." Department of Statistics and Mathematics, WU Vienna University of Economics and Business, 1999. http://epub.wu.ac.at/1790/1/document.pdf.

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We study the empirical quantization error in case the number of prototypes increases with the size of the sample. We present a proof of the consistency of the empirical quantization error and of corresponding estimators of the quantization dimensions of distributions. (author's abstract)
Series: Forschungsberichte / Institut für Statistik
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LaDue, Mark D. "Quantization error problems for classes of trigonometric polynomials." Diss., Georgia Institute of Technology, 1997. http://hdl.handle.net/1853/29176.

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Blanchard, Bart. "Quantization effects and implementation considerations for turbo decoders." [Gainesville, Fla.] : University of Florida, 2002. http://purl.fcla.edu/fcla/etd/UFE1000107.

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Thesis (M.S.)--University of Florida, 2002.
Title from title page of source document. Document formatted into pages; contains xiii, 91 p.; also contains graphics. Includes vita. Includes bibliographical references.
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Mao, Jie. "Reduction of the quantization error in fuzzy logic controllers by dithering." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ36717.pdf.

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SAWADA, Manabu, Hiraku OKADA, Takaya YAMAZATO, and Masaaki KATAYAMA. "Influence of ADC Nonlinearity on the Performance of an OFDM Receiver." IEICE, 2006. http://hdl.handle.net/2237/9582.

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Andersson, Tomas. "On error-robust source coding with image coding applications." Licentiate thesis, Stockholm : Department of Signals, Sensors and Systems, Royal Institute of Technology, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-4046.

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McMichael, Joseph Gary. "Timing offset and quantization error trade-off in interleaved multi-channel measurements." Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/66035.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 117-118).
Time-interleaved analog-to-digital converters (ADCs) are traditionally designed with equal quantization granularity in each channel and uniform sampling offsets. Recent work suggests that it is often possible to achieve a better signal-to-quantization noise ratio (SQNR) with different quantization granularity in each channel, non-uniform sampling, and appropriate reconstruction filtering. This thesis develops a framework for optimal design of non-uniform sampling constellations to maximize SQNR in time-interleaved ADCs. The first portion of this thesis investigates discrepancies between the additive noise model and uniform quantizers. A simulation is implemented for the multi-channel measurement and reconstruction system. The simulation reveals a key inconsistency in the environment of time-interleaved ADCs: cross-channel quantization error correlation. Statistical analysis is presented to characterize error correlation between quantizers with different granularities. A novel ADC architecture is developed based on weighted least squares (WLS) to exploit this correlation, with particular application for time-interleaved ADCs. A "correlated noise model" is proposed that incorporates error correlation between channels. The proposed model is shown to perform significantly better than the traditional additive noise model for channels in close proximity. The second portion of this thesis focuses on optimizing channel configurations in time-interleaved ADCs. Analytical and numerical optimization techniques are presented that rely on the additive noise model for determining non-uniform sampling constellations that maximize SQNR. Optimal constellations for critically sampled systems are always uniform, while solution sets for oversampled systems are larger. Systems with diverse bit allocations often exhibit "clusters" of low-precision channels in close proximity. Genetic optimization is shown to be effective for quickly and accurately determining optimal timing constellations in systems with many channels. Finally, a framework for efficient design of optimal channel configurations is formulated that incorporates statistical analysis of cross-channel quantization error correlation and solutions based on the additive noise model. For homogeneous bit allocations, the framework proposes timing offset corrections to avoid performance degradation from the optimal scenario predicted by the additive noise model. For diverse bit allocations, the framework proposes timing corrections and a "unification" of low-precision quantizers in close proximity. This technique results in significant improvements in performance above the previously known optimal additive noise model solution.
by Joseph Gary McMichael.
S.M.
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Wandeto, John Mwangi. "Self-organizing map quantization error approach for detecting temporal variations in image sets." Thesis, Strasbourg, 2018. http://www.theses.fr/2018STRAD025/document.

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Une nouvelle approche du traitement de l'image, appelée SOM-QE, qui exploite quantization error (QE) des self-organizing maps (SOM) est proposée dans cette thèse. Les SOM produisent des représentations discrètes de faible dimension des données d'entrée de haute dimension. QE est déterminée à partir des résultats du processus d'apprentissage non supervisé du SOM et des données d'entrée. SOM-QE d'une série chronologique d'images peut être utilisé comme indicateur de changements dans la série chronologique. Pour configurer SOM, on détermine la taille de la carte, la distance du voisinage, le rythme d'apprentissage et le nombre d'itérations dans le processus d'apprentissage. La combinaison de ces paramètres, qui donne la valeur la plus faible de QE, est considérée comme le jeu de paramètres optimal et est utilisée pour transformer l'ensemble de données. C'est l'utilisation de l'assouplissement quantitatif. La nouveauté de la technique SOM-QE est quadruple : d'abord dans l'usage. SOM-QE utilise un SOM pour déterminer la QE de différentes images - typiquement, dans un ensemble de données de séries temporelles - contrairement à l'utilisation traditionnelle où différents SOMs sont appliqués sur un ensemble de données. Deuxièmement, la valeur SOM-QE est introduite pour mesurer l'uniformité de l'image. Troisièmement, la valeur SOM-QE devient une étiquette spéciale et unique pour l'image dans l'ensemble de données et quatrièmement, cette étiquette est utilisée pour suivre les changements qui se produisent dans les images suivantes de la même scène. Ainsi, SOM-QE fournit une mesure des variations à l'intérieur de l'image à une instance dans le temps, et lorsqu'il est comparé aux valeurs des images subséquentes de la même scène, il révèle une visualisation transitoire des changements dans la scène à l'étude. Dans cette recherche, l'approche a été appliquée à l'imagerie artificielle, médicale et géographique pour démontrer sa performance. Les scientifiques et les ingénieurs s'intéressent aux changements qui se produisent dans les scènes géographiques d'intérêt, comme la construction de nouveaux bâtiments dans une ville ou le recul des lésions dans les images médicales. La technique SOM-QE offre un nouveau moyen de détection automatique de la croissance dans les espaces urbains ou de la progression des maladies, fournissant des informations opportunes pour une planification ou un traitement approprié. Dans ce travail, il est démontré que SOM-QE peut capturer de très petits changements dans les images. Les résultats confirment également qu'il est rapide et moins coûteux de faire la distinction entre le contenu modifié et le contenu inchangé dans les grands ensembles de données d'images. La corrélation de Pearson a confirmé qu'il y avait des corrélations statistiquement significatives entre les valeurs SOM-QE et les données réelles de vérité de terrain. Sur le plan de l'évaluation, cette technique a donné de meilleurs résultats que les autres approches existantes. Ce travail est important car il introduit une nouvelle façon d'envisager la détection rapide et automatique des changements, même lorsqu'il s'agit de petits changements locaux dans les images. Il introduit également une nouvelle méthode de détermination de QE, et les données qu'il génère peuvent être utilisées pour prédire les changements dans un ensemble de données de séries chronologiques
A new approach for image processing, dubbed SOM-QE, that exploits the quantization error (QE) from self-organizing maps (SOM) is proposed in this thesis. SOM produce low-dimensional discrete representations of high-dimensional input data. QE is determined from the results of the unsupervised learning process of SOM and the input data. SOM-QE from a time-series of images can be used as an indicator of changes in the time series. To set-up SOM, a map size, the neighbourhood distance, the learning rate and the number of iterations in the learning process are determined. The combination of these parameters that gives the lowest value of QE, is taken to be the optimal parameter set and it is used to transform the dataset. This has been the use of QE. The novelty in SOM-QE technique is fourfold: first, in the usage. SOM-QE employs a SOM to determine QE for different images - typically, in a time series dataset - unlike the traditional usage where different SOMs are applied on one dataset. Secondly, the SOM-QE value is introduced as a measure of uniformity within the image. Thirdly, the SOM-QE value becomes a special, unique label for the image within the dataset and fourthly, this label is used to track changes that occur in subsequent images of the same scene. Thus, SOM-QE provides a measure of variations within the image at an instance in time, and when compared with the values from subsequent images of the same scene, it reveals a transient visualization of changes in the scene of study. In this research the approach was applied to artificial, medical and geographic imagery to demonstrate its performance. Changes that occur in geographic scenes of interest, such as new buildings being put up in a city or lesions receding in medical images are of interest to scientists and engineers. The SOM-QE technique provides a new way for automatic detection of growth in urban spaces or the progressions of diseases, giving timely information for appropriate planning or treatment. In this work, it is demonstrated that SOM-QE can capture very small changes in images. Results also confirm it to be fast and less computationally expensive in discriminating between changed and unchanged contents in large image datasets. Pearson's correlation confirmed that there was statistically significant correlations between SOM-QE values and the actual ground truth data. On evaluation, this technique performed better compared to other existing approaches. This work is important as it introduces a new way of looking at fast, automatic change detection even when dealing with small local changes within images. It also introduces a new method of determining QE, and the data it generates can be used to predict changes in a time series dataset
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Burns, Jason R. "Effects of quantization error on the global positioning system software receiver interference mitigation." Ohio University / OhioLINK, 2002. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1174580139.

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Books on the topic "Quantization errors"

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Kollár, István, and Bernard Widrow. Quantization Noise: Roundoff Error in Digital Computation, Signal Processing, Control and Communications. Cambridge University Press, 2008.

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F, Huang Y., Stevenson Robert L, and United States. National Aeronautics and Space Administration., eds. [An efficient system for reliably transmitting image and video data over low bit rate noise]: An interim report. [Washington, DC: National Aeronautics and Space Administration, 1994.

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Book chapters on the topic "Quantization errors"

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King, Robert, Majid Ahmadi, Raouf Gorgui-Naguib, Alan Kwabwe, and Mahmood Azimi-Sadjadi. "Quantization and Roundoff Errors." In Digital Filtering in One and Two Dimensions, 197–217. Boston, MA: Springer US, 1989. http://dx.doi.org/10.1007/978-1-4899-0918-3_5.

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Conway, J. H., and N. J. A. Sloane. "Voronoi Cells of Lattices and Quantization Errors." In Grundlehren der mathematischen Wissenschaften, 451–77. New York, NY: Springer New York, 1999. http://dx.doi.org/10.1007/978-1-4757-6568-7_21.

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Conway, J. H., and N. J. A. Sloane. "Voronoi Cells of Lattices and Quantization Errors." In Grundlehren der mathematischen Wissenschaften, 449–75. New York, NY: Springer New York, 1993. http://dx.doi.org/10.1007/978-1-4757-2249-9_21.

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Conway, J. H., and N. J. A. Sloane. "Voronoi Cells of Lattices and Quantization Errors." In Grundlehren der mathematischen Wissenschaften, 449–75. New York, NY: Springer New York, 1988. http://dx.doi.org/10.1007/978-1-4757-2016-7_21.

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Paliwal, K. K., and B. S. Atal. "Vector Quantization of LPC Parameters in the Presence of Channel Errors." In Speech and Audio Coding for Wireless and Network Applications, 191–201. Boston, MA: Springer US, 1993. http://dx.doi.org/10.1007/978-1-4615-3232-3_25.

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Khayrallah, Ali. "Bounded expansion codes for error control." In Coding and Quantization, 225–33. Providence, Rhode Island: American Mathematical Society, 1993. http://dx.doi.org/10.1090/dimacs/014/26.

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Arikan, Erdal. "A bound on the zero-error list coding capacity." In Coding and Quantization, 235–41. Providence, Rhode Island: American Mathematical Society, 1993. http://dx.doi.org/10.1090/dimacs/014/27.

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Hirose, Kazutoshi, Kota Ando, Kodai Ueyoshi, Masayuki Ikebe, Tetsuya Asai, Masato Motomura, and Shinya Takamaeda-Yamazaki. "Quantization Error-Based Regularization in Neural Networks." In Artificial Intelligence XXXIV, 137–42. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-71078-5_11.

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Arunadevi, S., and S. Sathya. "Retracted: An Independent Reconstruction Error Using Randomized Quantization." In Lecture Notes in Electrical Engineering, 743–51. New Delhi: Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-2119-7_72.

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Nakamura, Masatoshi, Satoru Goto, and Nobuhiro Kyura. "4 Quantization Error of a Mechatronic Servo System." In Lecture Notes in Control and Information Sciences, 79–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-39921-6_4.

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Conference papers on the topic "Quantization errors"

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Chang, Ti-chiun, and Jan P. Allebach. "Quantization of accumulated diffused errors in error diffusion." In Electronic Imaging 2005, edited by Reiner Eschbach and Gabriel G. Marcu. SPIE, 2005. http://dx.doi.org/10.1117/12.584688.

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Bakr, Omar, Mark Johnson, Raghuraman Mudumbai, and Upamanyu Madhow. "Interference suppression in the presence of quantization errors." In 2009 47th Annual Allerton Conference on Communication, Control, and Computing (Allerton). IEEE, 2009. http://dx.doi.org/10.1109/allerton.2009.5394552.

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"BAYER PATTERN COMPRESSION BY PREDICTION ERRORS VECTOR QUANTIZATION." In 1st International Conference on E-business and Telecommunication Networks. SciTePress - Science and and Technology Publications, 2004. http://dx.doi.org/10.5220/0001392503250330.

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Li, Li, and Yudong Chen. "Quantization errors of modulo sigma-delta modulated ARMA processes." In 2013 IEEE China Summit and International Conference on Signal and Information Processing (ChinaSIP). IEEE, 2013. http://dx.doi.org/10.1109/chinasip.2013.6625303.

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Hoang, Tuan, Thanh-Toan Do, Tam V. Nguyen, and Ngai-Man Cheung. "Direct Quantization for Training Highly Accurate Low Bit-width Deep Neural Networks." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/292.

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This paper proposes two novel techniques to train deep convolutional neural networks with low bit-width weights and activations. First, to obtain low bit-width weights, most existing methods obtain the quantized weights by performing quantization on the full-precision network weights. However, this approach would result in some mismatch: the gradient descent updates full-precision weights, but it does not update the quantized weights. To address this issue, we propose a novel method that enables direct updating of quantized weights with learnable quantization levels to minimize the cost function using gradient descent. Second, to obtain low bit-width activations, existing works consider all channels equally. However, the activation quantizers could be biased toward a few channels with high-variance. To address this issue, we propose a method to take into account the quantization errors of individual channels. With this approach, we can learn activation quantizers that minimize the quantization errors in the majority of channels. Experimental results demonstrate that our proposed method achieves state-of-the-art performance on the image classification task, using AlexNet, ResNet and MobileNetV2 architectures on CIFAR-100 and ImageNet datasets.
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Sarmast, Mehdi, Saeed Bostan Manesh M., and Mahmood R. Mehran. "Sensitivity of NL-RDM to Errors in the Non-Linear Test Process." In ASME 2009 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. ASMEDC, 2009. http://dx.doi.org/10.1115/smasis2009-1275.

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NL-RDM is a non-linear system identification method that combines a number of linear and non-linear system identification methods and offers a practical approach to the identification of lumped parameter and continuous systems using a classical linear modal model with additional non-linear terms. The method was started by identifying the modal parameters of the underlying linear system via the FRF, MMIF and appropriated force vector. The criteria for an ideal method are detailed in the some earlier papers, but the reality creates a limitation. This paper is divided into several sections relating to the “Nonlinear Test Process”. Error which arise from test, environmental and equipment effects, are quantization errors, input (or process) noise and measurement noise. So, the effects of these inaccuracies and possible solutions for decreasing any negative effects are considered. Then, The sensitivities to noise and quantization which could be encountered in practical applications of the NL-RDM, are discussed in concept, generated, applied and analyzed through simulation programme for two degree of freedom uncoupled and coupled examples.
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Chaudhari, Sachin, and Visa Koivunen. "Effect of quantization and channel errors on collaborative spectrum sensing." In 2009 Conference Record of the Forty-Third Asilomar Conference on Signals, Systems and Computers. IEEE, 2009. http://dx.doi.org/10.1109/acssc.2009.5469883.

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Gvozdarev, Aleksey S., and Yury A. Bryukhanov. "Bit Depth Impact Analysis of the Gaussian Process Quantization Errors." In 2020 IEEE East-West Design & Test Symposium (EWDTS). IEEE, 2020. http://dx.doi.org/10.1109/ewdts50664.2020.9225108.

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Zhou, Jiantao, and Xiaolin Wu. "Context Modeling and Correction of Quantization Errors in Prediction Loop." In 2012 Data Compression Conference (DCC). IEEE, 2012. http://dx.doi.org/10.1109/dcc.2012.16.

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Kullaa, Jyrki. "REDUCTION OF QUANTIZATION AND CLIPPING ERRORS USING BAYESIAN VIRTUAL SENSORS." In XI International Conference on Structural Dynamics. Athens: EASD, 2020. http://dx.doi.org/10.47964/1120.9063.18788.

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