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Статті в журналах з теми "Predictive quantization"

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Автор: Grafiati

Опубліковано: 10 грудня 2022

Оновлено: 28 січня 2023

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1

Ngan, K. N., and H. C. Koh. "Predictive classified vector quantization." IEEE Transactions on Image Processing 1, no. 3 (July 1992): 269–80. http://dx.doi.org/10.1109/83.148602.

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2

Dubnov, Shlomo. "Predictive Quantization and Symbolic Dynamics." Algorithms 15, no. 12 (December 19, 2022): 484. http://dx.doi.org/10.3390/a15120484.

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Анотація:

Capturing long-term statistics of signals and time series is important for modeling recurrent phenomena, especially when such recurrences are a-periodic and can be characterized by the approximate repetition of variable length motifs, such as patterns in human gestures and trends in financial time series or musical melodies. Regressive and auto-regressive models that are common in such problems, both analytically derived and neural network-based, often suffer from limited memory or tend to accumulate errors, making them sensitive during training. Moreover, such models often assume stationary signal statistics, which makes it difficult to deal with switching regimes or conditional signal dynamics. In this paper, we describe a method for time series modeling that is based on adaptive symbolization that maximizes the predictive information of the resulting sequence. Using approximate string-matching methods, the initial vectorized sequence is quantized into a discrete representation with a variable quantization threshold. Finding an optimal signal embedding is formulated in terms of a predictive bottleneck problem that takes into account the trade-off between representation and prediction accuracy. Several downstream applications based on discrete representation are described in this paper, which includes an analysis of the symbolic dynamics of recurrence statistics, motif extraction, segmentation, query matching, and the estimation of transfer entropy between parallel signals.

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3

Klautau, A. B. R. "Predictive vector quantization with intrablock prediction support region." IEEE Transactions on Image Processing 8, no. 2 (1999): 293–95. http://dx.doi.org/10.1109/83.743862.

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4

Hsueh-Ming Hang and J. Woods. "Predictive Vector Quantization of Images." IEEE Transactions on Communications 33, no. 11 (November 1985): 1208–19. http://dx.doi.org/10.1109/tcom.1985.1096238.

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5

Linden, J. "Channel optimized predictive vector quantization." IEEE Transactions on Speech and Audio Processing 8, no. 4 (July 2000): 370–84. http://dx.doi.org/10.1109/89.848219.

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6

Rizvi, Syed A. "Entropy‐constrained predictive residual vector quantization." Optical Engineering 35, no. 1 (January 1, 1996): 187. http://dx.doi.org/10.1117/1.600889.

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7

Marcellin, M. W., T. R. Fischer, and J. D. Gibson. "Predictive trellis coded quantization of speech." IEEE Transactions on Acoustics, Speech, and Signal Processing 38, no. 1 (1990): 46–55. http://dx.doi.org/10.1109/29.45617.

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8

Schwarz, Stefan, and Markus Rupp. "Predictive Quantization on the Stiefel Manifold." IEEE Signal Processing Letters 22, no. 2 (February 2015): 234–38. http://dx.doi.org/10.1109/lsp.2014.2354258.

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9

Wu, Yung-Gi. "Predictive classifier for image vector quantization." Optical Engineering 39, no. 9 (September 1, 2000): 2372. http://dx.doi.org/10.1117/1.1286465.

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10

Rizvi, S. A., and N. M. Nasrabadi. "Predictive residual vector quantization [image coding]." IEEE Transactions on Image Processing 4, no. 11 (1995): 1482–95. http://dx.doi.org/10.1109/83.469930.

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11

Yang, Erkun, Cheng Deng, Chao Li, Wei Liu, Jie Li, and Dacheng Tao. "Shared Predictive Cross-Modal Deep Quantization." IEEE Transactions on Neural Networks and Learning Systems 29, no. 11 (November 2018): 5292–303. http://dx.doi.org/10.1109/tnnls.2018.2793863.

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12

Qu, Hongchun, Yu Li, and Wei Liu. "Output Feedback Model Predictive Control for NCSs with Input Quantization." Complexity 2022 (April 22, 2022): 1–20. http://dx.doi.org/10.1155/2022/6929902.

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Анотація:

This paper addresses the robust output feedback model predictive control (MPC) schemes for networked control systems (NCSs) with input quantization. The logarithmic quantizer is considered in this paper, and the sector bound approach is applied, which appropriately treats the quantization error as a sector-bounded uncertainty. The presented method involves an offline designed state observer using linear matrix inequality (LMI) and online robust output feedback MPC algorithms which optimize one free control move followed by the output feedback using the estimated state. Moreover, due to the uncertainty of estimation error, a technique of refreshing the bound of estimation error which involves the quantization error is provided so as to guarantee the recursive feasibility of the optimization problem. The proposed MPC schemes inherit the characteristics of the synthesis approach of MPC, guaranteeing the recursive feasibility of the optimization problem and the stability of a closed-loop system, and explicitly account for quantization error. Two simulation examples are given to illustrate the effectiveness of the proposed methods.

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13

Almazaideh, Mohammed, and Janos Levendovszky. "A predictive maintenance system for wireless sensor networks: a machine learning approach." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 2 (February 1, 2022): 1047. http://dx.doi.org/10.11591/ijeecs.v25.i2.pp1047-1058.

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Анотація:

<p>Predictive maintenance system (PdM) is a new concept that helps system operators evaluate the current status of their systems, and it also assists in predicting the future quality of these systems and scheduling maintenance action. This paper proposes a PdM model that utilizes machine learning to predict the system’s operational status after M active steps based on L previous observations implemented by a feedforward neural network (FFNN). We use quantization and encoding schemes to reduce the complexity of the system. We apply the proposed model to build a PdM system for wireless sensors networks (WSNs), where our concern is to predict the state of the system as far as the quality of data transfer is concerned. The FFNN provides a forward prediction of the operational status of the network after M consecutive time steps in the future, based on the previous L readings of quality of service (QoS) requirements of WSN. We also demonstrate the relation between complexity and accuracy. We found that larger M leads to higher complexity and larger prediction error, where larger L entails higher complexity and smaller prediction error. We also investigate how quantization and encoding can reduce complexity to implement a real-time PdM system.</p>

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14

Chang, Chin-Chen. "Predictive subcodebook search algorithm for vector quantization." Optical Engineering 39, no. 6 (June 1, 2000): 1489. http://dx.doi.org/10.1117/1.602521.

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15

Rin Chul Kim and Sang Uk Lee. "Entropy constrained predictive vector quantization of speech." Signal Processing 28, no. 1 (July 1992): 77–90. http://dx.doi.org/10.1016/0165-1684(92)90066-6.

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16

Memon, N. D., and K. Sayood. "Scan predictive vector quantization of multispectral images." IEEE Transactions on Image Processing 5, no. 2 (1996): 330–37. http://dx.doi.org/10.1109/83.480768.

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17

Gupta, S., and A. Gersho. "Feature predictive vector quantization of multispectral images." IEEE Transactions on Geoscience and Remote Sensing 30, no. 3 (May 1992): 491–501. http://dx.doi.org/10.1109/36.142927.

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18

Tang, Xiao-Ming, Hong-Chun Qu, Hao-Fei Xie, and Ping Wang. "Model Predictive Control of Linear Systems over Networks with State and Input Quantizations." Mathematical Problems in Engineering 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/492804.

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Анотація:

Although there have been a lot of works about the synthesis and analysis of networked control systems (NCSs) with data quantization, most of the results are developed for the case of considering the quantizer only existing in one of the transmission links (either from the sensor to the controller link or from the controller to the actuator link). This paper investigates the synthesis approaches of model predictive control (MPC) for NCS subject to data quantizations in both links. Firstly, a novel model to describe the state and input quantizations of the NCS is addressed by extending the sector bound approach. Further, from the new model, two synthesis approaches of MPC are developed: one parameterizes the infinite horizon control moves into a single state feedback law and the other into a free control move followed by the single state feedback law. Finally, the stability results that explicitly consider the satisfaction of input and state constraints are presented. A numerical example is given to illustrate the effectiveness of the proposed MPC.

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19

Yu, Jimin, Yanan Xie, and Xiaoming Tang. "Model Predictive Control of NCS with Data Quantization and Bounded Arbitrary Time Delays." Journal of Control Science and Engineering 2015 (2015): 1–13. http://dx.doi.org/10.1155/2015/259480.

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Анотація:

The model predictive control for constrained discrete time linear system under network environment is considered. The bounded time delay and data quantization are assumed to coexist in the data transmission link from the sensor to the controller. A novel NCS model is specially established for the model predictive control method, which casts the time delay and data quantization into a unified framework. A stability result of the obtained closed-loop model is presented by applying the Lyapunov method, which plays a key role in synthesizing the model predictive controller. The model predictive controller, which parameterizes the infinite horizon control moves into a single state feedback law, is provided which explicitly considers the satisfaction of input and state constraints. Two numerical examples are given to illustrate the effectiveness of the derived method.

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20

Ikuma, Takeshi, Mort Naraghi-Pour, and Thomas Lewis. "Predictive Quantization of Range-Focused SAR Raw Data." IEEE Transactions on Geoscience and Remote Sensing 50, no. 4 (April 2012): 1340–48. http://dx.doi.org/10.1109/tgrs.2011.2167236.

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21

Modestino, J. W., and Y. H. Kim. "Adaptive entropy-coded predictive vector quantization of images." IEEE Transactions on Signal Processing 40, no. 3 (March 1992): 633–44. http://dx.doi.org/10.1109/78.120806.

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22

Kuo, Chung-Ming. "Predictive search algorithm for vector quantization of images." Optical Engineering 41, no. 5 (May 1, 2002): 1104. http://dx.doi.org/10.1117/1.1467937.

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23

Poggi, G. "Generalized-cost-measure-based address-predictive vector quantization." IEEE Transactions on Image Processing 5, no. 1 (January 1996): 49–55. http://dx.doi.org/10.1109/83.481670.

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24

Mohsenian, Nader. "Predictive vector quantization using a neural network approach." Optical Engineering 32, no. 7 (1993): 1503. http://dx.doi.org/10.1117/12.141678.

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25

Zou, Hang, Fengjun Zhao, Xiaoxue Jia, Heng Zhang, and Wei Wang. "Fusion of dynamic predictive block adaptive quantization and vector quantization for staggered SAR data compression." Remote Sensing Letters 12, no. 2 (December 28, 2020): 206–15. http://dx.doi.org/10.1080/2150704x.2020.1851796.

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26

Bolcskei, H., and F. Hlawatsch. "Noise reduction in oversampled filter banks using predictive quantization." IEEE Transactions on Information Theory 47, no. 1 (2001): 155–72. http://dx.doi.org/10.1109/18.904519.

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27

Bage, M. "Interframe Predictive Coding of Images Using Hybrid Vector Quantization." IEEE Transactions on Communications 34, no. 4 (April 1986): 411–15. http://dx.doi.org/10.1109/tcom.1986.1096537.

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28

Ramirez, Miguel Arjona. "Intra-Predictive Switched Split Vector Quantization of Speech Spectra." IEEE Signal Processing Letters 20, no. 8 (August 2013): 791–94. http://dx.doi.org/10.1109/lsp.2013.2267391.

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29

Fletcher, Alyson K., Sundeep Rangan, Vivek K. Goyal, and Kannan Ramchandran. "Robust Predictive Quantization: Analysis and Design Via Convex Optimization." IEEE Journal of Selected Topics in Signal Processing 1, no. 4 (December 2007): 618–32. http://dx.doi.org/10.1109/jstsp.2007.910622.

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30

Canta, Gerardo R., and Giovanni Poggi. "Compression of multispectral images by address-predictive vector quantization." Signal Processing: Image Communication 11, no. 2 (December 1997): 147–59. http://dx.doi.org/10.1016/s0923-5965(96)00043-4.

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31

Xu, Jie, Hao Jiang, and Zhen Li. "3D Mesh Compression by Generalized Parallelogram Predictive Vector Quantization." Information Technology Journal 10, no. 4 (March 15, 2011): 877–82. http://dx.doi.org/10.3923/itj.2011.877.882.

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32

Chen, Qunlin, Derong Chen, and Jiulu Gong. "A General Rate-Distortion Optimization Method for Block Compressed Sensing of Images." Entropy 23, no. 10 (October 16, 2021): 1354. http://dx.doi.org/10.3390/e23101354.

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Анотація:

Block compressed sensing (BCS) is a promising technology for image sampling and compression for resource-constrained applications, but it needs to balance the sampling rate and quantization bit-depth for a bit-rate constraint. In this paper, we summarize the commonly used CS quantization frameworks into a unified framework, and a new bit-rate model and a model of the optimal bit-depth are proposed for the unified CS framework. The proposed bit-rate model reveals the relationship between the bit-rate, sampling rate, and bit-depth based on the information entropy of generalized Gaussian distribution. The optimal bit-depth model can predict the optimal bit-depth of CS measurements at a given bit-rate. Then, we propose a general algorithm for choosing sampling rate and bit-depth based on the proposed models. Experimental results show that the proposed algorithm achieves near-optimal rate-distortion performance for the uniform quantization framework and predictive quantization framework in BCS.

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33

Wu, Yung-Gi. "Fast vector quantization image coding by mean value predictive algorithm." Journal of Electronic Imaging 13, no. 2 (April 1, 2004): 324. http://dx.doi.org/10.1117/1.1666877.

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34

Bhagavatula, Ramya, and Robert W. Heath. "Predictive Vector Quantization for Multicell Cooperation with Delayed Limited Feedback." IEEE Transactions on Wireless Communications 12, no. 6 (June 2013): 2588–97. http://dx.doi.org/10.1109/twc.2013.040413.112037.

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35

Zhu, Ce. "A new subsampling-based predictive vector quantization for image coding." Signal Processing: Image Communication 17, no. 6 (July 2002): 477–84. http://dx.doi.org/10.1016/s0923-5965(02)00022-x.

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36

Martone, Michele, Nicola Gollin, Michelangelo Villano, Paola Rizzoli, and Gerhard Krieger. "Predictive Quantization for Data Volume Reduction in Staggered SAR Systems." IEEE Transactions on Geoscience and Remote Sensing 58, no. 8 (August 2020): 5575–87. http://dx.doi.org/10.1109/tgrs.2020.2967450.

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37

Rizvi, S. A., L. C. Wang, and N. M. Nasrabadi. "Rate-constrained modular predictive residual vector quantization of digital images." IEEE Signal Processing Letters 6, no. 6 (June 1999): 135–37. http://dx.doi.org/10.1109/97.763144.

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38

Abousleman, G. P., M. W. Marcellin, and B. R. Hunt. "Hyperspectral image compression using entropy-constrained predictive trellis coded quantization." IEEE Transactions on Image Processing 6, no. 4 (April 1997): 566–73. http://dx.doi.org/10.1109/83.563321.

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39

Wang, Jian, and Golshah Naghdy. "A new shape-vector quantization-based adaptive predictive image coder." International Journal of Imaging Systems and Technology 10, no. 6 (1999): 419–26. http://dx.doi.org/10.1002/(sici)1098-1098(1999)10:6<419::aid-ima2>3.0.co;2-5.

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40

Wang, Siyao, Xiaoming Tang, Li Deng, Hongchun Qu, Linfeng Tian, and Cheng Tan. "Predictive Control for Interval Type-2 Fuzzy System with Event-Triggered Scheme." Advances in Fuzzy Systems 2019 (July 8, 2019): 1–13. http://dx.doi.org/10.1155/2019/9365767.

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Анотація:

In this paper, a synthesis approach of model predictive control (MPC) is proposed for interval type-2 (IT2) Takagi-Sugeno (T-S) fuzzy system with quantization error, bounded disturbance, and data loss. The novelty lies in the following technical improvements. In order to reduce the redundant data transmission, an event-triggered communication scheme is applied to determine whether the control law should be transmitted into the communication network or not. The IT2 T-S fuzzy model is utilized to address the nonlinearity of plant with parameter uncertainties, which can be captured by the lower and upper membership functions. Furthermore, the phenomena of data loss and quantization error between the controller and the actuator are expressed as Markovian chain and sector-bound uncertainties. The synthesis approach of MPC is provided by solving an MPC optimization problem over an infinite horizon objective function which explicitly considers the input constraints. By applying the quadratic boundedness (QB) technique, the recursive feasibility and quadratic stability of closed-loop system can be guaranteed. A numerical simulation and comparison studies are proposed to illustrate the effectiveness of this approach.

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41

Lu, Min. "A Novel Predictive Quantization Feedback Scheme for Zero-Forcing Beamforming System." Journal of Information and Computational Science 10, no. 8 (May 20, 2013): 2409–16. http://dx.doi.org/10.12733/jics20102228.

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42

Kim, Y. H., and J. W. Modestino. "Adaptive entropy-coded pruned tree-structured predictive vector quantization of images." IEEE Transactions on Communications 41, no. 1 (1993): 171–85. http://dx.doi.org/10.1109/26.212377.

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43

Chen, Juin-Hwey. "Speech signal quantization using human auditory models in predictive coding systems." Journal of the Acoustical Society of America 104, no. 3 (1998): 1155. http://dx.doi.org/10.1121/1.424308.

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44

Kang, S., Y. Shin, and T. R. Fischer. "Low-Complexity Predictive Trellis-Coded Quantization of Speech Line Spectral Frequencies." IEEE Transactions on Signal Processing 52, no. 7 (July 2004): 2070–79. http://dx.doi.org/10.1109/tsp.2004.828916.

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45

Aarskog, A. I., and H. C. Guren. "Predictive coding of speech using microphone/speaker adaptation and vector quantization." IEEE Transactions on Speech and Audio Processing 2, no. 2 (April 1994): 266–73. http://dx.doi.org/10.1109/89.279275.

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46

Sakalli, Mustafa, Hong Yan, and Alan Fu. "A Region-Based Scheme Using RKLT and Predictive Classified Vector Quantization." Computer Vision and Image Understanding 75, no. 3 (September 1999): 269–80. http://dx.doi.org/10.1006/cviu.1999.0776.

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47

Shinohara, Katsuyuki, and Toshi Minami. "Encoding of still pictures with successive clustering and predictive vector quantization." Electronics and Communications in Japan (Part I: Communications) 71, no. 6 (June 1988): 98–110. http://dx.doi.org/10.1002/ecja.4410710610.

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48

Poggi, Giovanni. "Address-Predictive Vector Quantization of Images by Topology-Preserving Codebook Ordering." European Transactions on Telecommunications 4, no. 4 (July 1993): 423–34. http://dx.doi.org/10.1002/ett.4460040408.

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49

Carlini, Paolo. "Self Organizing Maps, Vector Quantization, and Fractal Image Coding." Fractals 05, supp01 (April 1997): 201–14. http://dx.doi.org/10.1142/s0218348x97000760.

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Анотація:

This paper reviews the properties of a popular kind of neural network, called self organizing maps, then discusses their relevance for the field of lossy image coding. In particular, vector quantization (VQ) and fractal (block based) coding are studied from a common point of view, emphasizing their relationships. The latter is an exotic kind of (non-causal) predictive-VQ; clustering algorithms, directly useful in the VQ field, can be exploited to reduce its computational complexity.

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50

Wei, Shih-Chieh, and Bormin Huang. "GPU Acceleration of Predictive Partitioned Vector Quantization for Ultraspectral Sounder Data Compression." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 4, no. 3 (September 2011): 677–82. http://dx.doi.org/10.1109/jstars.2011.2132117.

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