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Journal articles on the topic 'Stochastic processing networks'

Author: Grafiati

Published: 4 June 2021

Last updated: 14 February 2022

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1

Williams, Ruth J. "Stochastic Processing Networks." Annual Review of Statistics and Its Application 3, no. 1 (2016): 323–45. http://dx.doi.org/10.1146/annurev-statistics-010814-020141.

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2

Shah, Devavrat. "Message-passing in stochastic processing networks." Surveys in Operations Research and Management Science 16, no. 2 (2011): 83–104. http://dx.doi.org/10.1016/j.sorms.2011.03.002.

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3

Dai, J. G., and Wuqin Lin. "Maximum Pressure Policies in Stochastic Processing Networks." Operations Research 53, no. 2 (2005): 197–218. http://dx.doi.org/10.1287/opre.1040.0170.

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4

Kella, Offer, and Ward Whitt. "Linear stochastic fluid networks." Journal of Applied Probability 36, no. 01 (1999): 244–60. http://dx.doi.org/10.1017/s0021900200017009.

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We introduce open stochastic fluid networks that can be regarded as continuous analogues or fluid limits of open networks of infinite-server queues. Random exogenous input may come to any of the queues. At each queue, a c.d.f.-valued stochastic process governs the proportion of the input processed by a given time after arrival. The routeing may be deterministic (a specified sequence of successive queue visits) or proportional, i.e. a stochastic transition matrix may govern the proportion of the output routed from one queue to another. This stochastic fluid network with deterministic c.d.f.s go

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5

Kella, Offer, and Ward Whitt. "Linear stochastic fluid networks." Journal of Applied Probability 36, no. 1 (1999): 244–60. http://dx.doi.org/10.1239/jap/1032374245.

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We introduce open stochastic fluid networks that can be regarded as continuous analogues or fluid limits of open networks of infinite-server queues. Random exogenous input may come to any of the queues. At each queue, a c.d.f.-valued stochastic process governs the proportion of the input processed by a given time after arrival. The routeing may be deterministic (a specified sequence of successive queue visits) or proportional, i.e. a stochastic transition matrix may govern the proportion of the output routed from one queue to another. This stochastic fluid network with deterministic c.d.f.s go

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6

Gao, Zhan, Elvin Isufi, and Alejandro Ribeiro. "Stochastic Graph Neural Networks." IEEE Transactions on Signal Processing 69 (2021): 4428–43. http://dx.doi.org/10.1109/tsp.2021.3092336.

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7

Ziv, Etay, Ilya Nemenman, and Chris H. Wiggins. "Optimal Signal Processing in Small Stochastic Biochemical Networks." PLoS ONE 2, no. 10 (2007): e1077. http://dx.doi.org/10.1371/journal.pone.0001077.

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8

Bassamboo, Achal, J. Michael Harrison, and Assaf Zeevi. "Pointwise Stationary Fluid Models for Stochastic Processing Networks." Manufacturing & Service Operations Management 11, no. 1 (2009): 70–89. http://dx.doi.org/10.1287/msom.1070.0195.

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9

Yuan, Yuan, Kui Wu, Weijia Jia, and Yuming Jiang. "Performance of Acyclic Stochastic Networks with Network Coding." IEEE Transactions on Parallel and Distributed Systems 22, no. 7 (2011): 1238–45. http://dx.doi.org/10.1109/tpds.2010.192.

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10

Afèche, Philipp. "Delay performance in stochastic processing networks with priority service." Operations Research Letters 31, no. 5 (2003): 390–400. http://dx.doi.org/10.1016/s0167-6377(03)00021-x.

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11

Jones, C. Kenneth. "Fixed trading costs, signal processing and stochastic portfolio networks." European J. of Industrial Engineering 1, no. 1 (2007): 5. http://dx.doi.org/10.1504/ejie.2007.012651.

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12

Harrison, J. M., and R. J. Williams. "Workload Interpretation for Brownian Models of Stochastic Processing Networks." Mathematics of Operations Research 32, no. 4 (2007): 808–20. http://dx.doi.org/10.1287/moor.1070.0271.

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13

Bowsher, Clive G. "Information processing by biochemical networks: a dynamic approach." Journal of The Royal Society Interface 8, no. 55 (2010): 186–200. http://dx.doi.org/10.1098/rsif.2010.0287.

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Understanding how information is encoded and transferred by biochemical networks is of fundamental importance in cellular and systems biology. This requires analysis of the relationships between the stochastic trajectories of the constituent molecular (or submolecular) species that comprise the network. We describe how to identify conditional independences between the trajectories or time courses of groups of species. These are robust network properties that provide important insight into how information is processed. An entire network can then be decomposed exactly into modules on information

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14

Dai, J. G., and Wuqin Lin. "Asymptotic optimality of maximum pressure policies in stochastic processing networks." Annals of Applied Probability 18, no. 6 (2008): 2239–99. http://dx.doi.org/10.1214/08-aap522.

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15

Pedarsani, Ramtin, Jean Walrand, and Yuan Zhong. "Robust scheduling for flexible processing networks." Advances in Applied Probability 49, no. 2 (2017): 603–28. http://dx.doi.org/10.1017/apr.2017.14.

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Abstract Modern processing networks often consist of heterogeneous servers with widely varying capabilities, and process job flows with complex structure and requirements. A major challenge in designing efficient scheduling policies in these networks is the lack of reliable estimates of system parameters, and an attractive approach for addressing this challenge is to design robust policies, i.e. policies that do not use system parameters such as arrival and/or service rates for making scheduling decisions. In this paper we propose a general framework for the design of robust policies. The main

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16

Zhu, Zhigang, Hongbing Ji, Wenbo Zhang, and Guopeng Huang. "Temporal stochastic linear encoding networks." Signal Processing: Image Communication 70 (February 2019): 14–20. http://dx.doi.org/10.1016/j.image.2018.09.001.

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17

Hong, Mingyi, and Tsung-Hui Chang. "Stochastic Proximal Gradient Consensus Over Random Networks." IEEE Transactions on Signal Processing 65, no. 11 (2017): 2933–48. http://dx.doi.org/10.1109/tsp.2017.2673815.

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18

Tosh, Colin R., and Graeme D. Ruxton. "The need for stochastic replication of ecological neural networks." Philosophical Transactions of the Royal Society B: Biological Sciences 362, no. 1479 (2007): 455–60. http://dx.doi.org/10.1098/rstb.2006.1973.

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Artificial neural networks are becoming increasingly popular as predictive statistical tools in ecosystem ecology and as models of signal processing in behavioural and evolutionary ecology. We demonstrate here that a commonly used network in ecology, the three-layer feed-forward network, trained with the backpropagation algorithm, can be extremely sensitive to the stochastic variation in training data that results from random sampling of the same underlying statistical distribution, with networks converging to several distinct predictive states. Using a random walk procedure to sample error–we

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19

Ying-Chao Hung and G. Michailidis. "Stability and Control of Acyclic Stochastic Processing Networks With Shared Resources." IEEE Transactions on Automatic Control 57, no. 2 (2012): 489–94. http://dx.doi.org/10.1109/tac.2011.2164012.

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20

Xu, Kevin S., and Alfred O. Hero. "Dynamic Stochastic Blockmodels for Time-Evolving Social Networks." IEEE Journal of Selected Topics in Signal Processing 8, no. 4 (2014): 552–62. http://dx.doi.org/10.1109/jstsp.2014.2310294.

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21

Pavlovic, V., D. Schonfeld, and G. Friedman. "Stochastic noise Process enhancement of Hopfield neural networks." IEEE Transactions on Circuits and Systems II: Express Briefs 52, no. 4 (2005): 213–17. http://dx.doi.org/10.1109/tcsii.2004.842027.

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22

Fan, Tongke. "Research on Optimal CDMA Multiuser Detection Based on Stochastic Hopfield Neural Network." Recent Patents on Computer Science 12, no. 3 (2019): 233–40. http://dx.doi.org/10.2174/2213275912666181210103742.

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Background: Most of the common multi-user detection techniques have the shortcomings of large computation and slow operation. For Hopfield neural networks, there are some problems such as high-speed searching ability and parallel processing, but there are local convergence problems. Objective: The stochastic Hopfield neural network avoids local convergence by introducing noise into the state variables and then achieves the optimal detection. Methods: Based on the study of CDMA communication model, this paper presents and models the problem of multi-user detection. Then a new stochastic Hopfiel

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23

Shareef, Ali, and Yifeng Zhu. "Effective Stochastic Modeling of Energy-Constrained Wireless Sensor Networks." Journal of Computer Networks and Communications 2012 (2012): 1–20. http://dx.doi.org/10.1155/2012/870281.

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Energy consumption of energy-constrained nodes in wireless sensor networks (WSNs) is a fatal weakness of these networks. Since these nodes usually operate on batteries, the maximum utility of the network is dependent upon the optimal energy usage of these nodes. However, new emerging optimal energy consumption algorithms, protocols, and system designs require an evaluation platform. This necessitates modeling techniques that can quickly and accurately evaluate their behavior and identify strengths and weakness. We propose Petri nets as this ideal platform. We demonstrate Petri net models of wi

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24

Galán-Prado, Fabio, Alejandro Morán, Joan Font, Miquel Roca, and Josep L. Rosselló. "Compact Hardware Synthesis of Stochastic Spiking Neural Networks." International Journal of Neural Systems 29, no. 08 (2019): 1950004. http://dx.doi.org/10.1142/s0129065719500047.

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Spiking neural networks (SNN) are able to emulate real neural behavior with high confidence due to their bio-inspired nature. Many designs have been proposed for the implementation of SNN in hardware, although the realization of high-density and biologically-inspired SNN is currently a complex challenge of high scientific and technical interest. In this work, we propose a compact digital design for the implementation of high-volume SNN that considers the intrinsic stochastic processes present in biological neurons and enables high-density hardware implementation. The proposed stochastic SNN mo

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25

Lee, Junghoon, and Cihan Tepedelenlioglu. "Stochastic Ordering of Interference in Large-Scale Wireless Networks." IEEE Transactions on Signal Processing 62, no. 3 (2014): 729–40. http://dx.doi.org/10.1109/tsp.2013.2293977.

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26

CARD, HOWARD C. "STOCHASTIC RADIAL BASIS FUNCTIONS." International Journal of Neural Systems 11, no. 02 (2001): 203–10. http://dx.doi.org/10.1142/s0129065701000552.

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Stochastic signal processing can implement gaussian activation functions for radial basis function networks, using stochastic counters. The statistics of neural inputs which control the increment and decrement operations of the counter are governed by Bernoulli distributions. The transfer functions relating the input and output pulse probabilities can closely approximate gaussian activation functions which improve with the number of states in the counter. The means and variances of these gaussian approximations can be controlled by varying the output combinational logic function of the binary

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27

Rossa, Fabio Della, and Pietro DeLellis. "Stochastic Pinning Controllability of Noisy Complex Networks." IEEE Transactions on Control of Network Systems 7, no. 4 (2020): 1678–87. http://dx.doi.org/10.1109/tcns.2020.2995818.

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28

KIM, Hyung Seok, Seok LEE, and Namhoon KIM. "Stochastic Congestion Control in Wireless Sensor Networks." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E93-A, no. 1 (2010): 344–47. http://dx.doi.org/10.1587/transfun.e93.a.344.

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29

Akbar, Mutaqin. "Traffic sign recognition using convolutional neural networks." Jurnal Teknologi dan Sistem Komputer 9, no. 2 (2021): 120–25. http://dx.doi.org/10.14710/jtsiskom.2021.13959.

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Traffic sign recognition (TSR) can be used to recognize traffic signs by utilizing image processing. This paper presents traffic sign recognition in Indonesia using convolutional neural networks (CNN). The overall image dataset used is 2050 images of traffic signs, consisting of 10 kinds of signs. The CNN layer used in this study consists of one convolution layer, one pooling layer using maxpool operation, and one fully connected layer. The training algorithm used is stochastic gradient descent (SGD). At the training stage, using 1750 training images, 48 filters, and a learning rate of 0.005,

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30

Meyer, Carsten, and Carl van Vreeswijk. "Temporal Correlations in Stochastic Networks of Spiking Neurons." Neural Computation 14, no. 2 (2002): 369–404. http://dx.doi.org/10.1162/08997660252741167.

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The determination of temporal and spatial correlations in neuronal activity is one of the most important neurophysiological tools to gain insight into the mechanisms of information processing in the brain. Its interpretation is complicated by the difficulty of disambiguating the effects of architecture, single-neuron properties, and network dynamics. We present a theory that describes the contribution of the network dynamics in a network of “spiking” neurons. For a simple neuron model including refractory properties, we calculate the temporal cross-correlations in a completely homogeneous, exc

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31

Gieroba, Robert, and Łukasz Kruk. "Minimality of SRPT Networks With Resource Sharing." WSEAS TRANSACTIONS ON MATHEMATICS 20 (March 19, 2021): 74–83. http://dx.doi.org/10.37394/23206.2021.20.8.

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A general multi-resource network with users requiring service from a number of shared resources simultaneously is considered. It is demonstrated that the Shortest Remaining Processing Time (SRPT) service protocol minimizes, in a suitable sense, the system resource idleness with respect to customers with residual service times not greater than any threshold value on every network route. Our arguments are pathwise, with no assumptions on the model stochastic primitives and the network topology.

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32

Calvo-Fullana, Miguel, Carles Anton-Haro, Javier Matamoros, and Alejandro Ribeiro. "Stochastic Routing and Scheduling Policies for Energy Harvesting Communication Networks." IEEE Transactions on Signal Processing 66, no. 13 (2018): 3363–76. http://dx.doi.org/10.1109/tsp.2018.2833814.

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33

Bedi, Amrit Singh, Alec Koppel, and Ketan Rajawat. "Asynchronous Saddle Point Algorithm for Stochastic Optimization in Heterogeneous Networks." IEEE Transactions on Signal Processing 67, no. 7 (2019): 1742–57. http://dx.doi.org/10.1109/tsp.2019.2894803.

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34

Cadena, Jose, Priyadip Ray, Hao Chen, et al. "Stochastic Gradient-Based Distributed Bayesian Estimation in Cooperative Sensor Networks." IEEE Transactions on Signal Processing 69 (2021): 1713–24. http://dx.doi.org/10.1109/tsp.2021.3058765.

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35

Huang, Changqin, Qionghao Huang, and Dianhui Wang. "Stochastic Configuration Networks Based Adaptive Storage Replica Management for Power Big Data Processing." IEEE Transactions on Industrial Informatics 16, no. 1 (2020): 373–83. http://dx.doi.org/10.1109/tii.2019.2919268.

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36

Dieker, A. B., and J. Shin. "From Local to Global Stability in Stochastic Processing Networks Through Quadratic Lyapunov Functions." Mathematics of Operations Research 38, no. 4 (2013): 638–64. http://dx.doi.org/10.1287/moor.2013.0588.

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37

Goldstein, William M., and Donald L. Fisher. "Stochastic networks as models of cognition: Deriving predictions for resource-constrained mental processing." Journal of Mathematical Psychology 36, no. 1 (1992): 129–45. http://dx.doi.org/10.1016/0022-2496(92)90055-c.

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38

Blachowicz, Tomasz, Jacek Grzybowski, Pawel Steblinski, and Andrea Ehrmann. "Neuro-Inspired Signal Processing in Ferromagnetic Nanofibers." Biomimetics 6, no. 2 (2021): 32. http://dx.doi.org/10.3390/biomimetics6020032.

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Computers nowadays have different components for data storage and data processing, making data transfer between these units a bottleneck for computing speed. Therefore, so-called cognitive (or neuromorphic) computing approaches try combining both these tasks, as is done in the human brain, to make computing faster and less energy-consuming. One possible method to prepare new hardware solutions for neuromorphic computing is given by nanofiber networks as they can be prepared by diverse methods, from lithography to electrospinning. Here, we show results of micromagnetic simulations of three coup

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39

Amosov, Oleg Semenovich, and Svetlana Gennadievna Amosova. "Peculiarities and Applications of Stochastic Processes with Fractal Properties." Sensors 21, no. 17 (2021): 5960. http://dx.doi.org/10.3390/s21175960.

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In this paper, the fractal properties of stochastic processes and objects in different areas were specified and investigated. These included: measuring systems and sensors, navigation and motion controls, telecommunication systems and networks, and flaw detection technologies. Additional options that occur through the use of fractality were also indicated and exemplified for each application. Regarding the problems associated with navigation information processing, the following fractal nature processes were identified: errors of inertial sensors based on the microelectromechanical systems cal

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40

Hu, Xin, Chi Huang, Jianquan Lu, and Jinde Cao. "Stabilization of Boolean control networks with stochastic impulses." Journal of the Franklin Institute 356, no. 13 (2019): 7164–82. http://dx.doi.org/10.1016/j.jfranklin.2019.06.039.

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41

Joshi, T., D. Ahuja, D. Singh, and D. P. Agrawal. "SARA: Stochastic Automata Rate Adaptation for IEEE 802.11 Networks." IEEE Transactions on Parallel and Distributed Systems 19, no. 11 (2008): 1579–90. http://dx.doi.org/10.1109/tpds.2007.70814.

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42

Song, Liang, Guihua Li, and Shaodong Chen. "Solving Nonlinear Wave Equation Based on Topology." International Journal of Circuits, Systems and Signal Processing 15 (August 31, 2021): 1232–41. http://dx.doi.org/10.46300/9106.2021.15.134.

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A method of solving nonlinear wave equation based on topology is proposed. Firstly, the characteristics of stochastic graph and Scaleless network are compared, and their topological characteristics are analyzed. Because of the existence of a few axis nodes, Scaleless networks have higher average aggregation than those with the same number of airport nodes and connected stochastic graphs. According to the topological structure of nonlinear wave equation, the first-order integral method is used to solve the nonlinear wave equation. According to the first integration, the threshold range is set,

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43

POSTMA, ERIC O., H. JAAP VAN DEN HERIK, and PATRICK T. W. HUDSON. "ROBUST FEEDFORWARD PROCESSING IN SYNFIRE CHAINS." International Journal of Neural Systems 07, no. 04 (1996): 537–42. http://dx.doi.org/10.1142/s012906579600052x.

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The rapidity of time-constrained visual identification suggests a feedforward process in which neural activity is propagated through a number of cortical stages. The process is modeled by using a synfire chain, leading to a neural-network model which involves propagating activation waves through a sequence of layers. Theory and analysis of the model’s behavior, especially in the presence of noise, predict enhancement of wave propagation for a range of noise intensities. Simulation studies confirm this prediction. The results are discussed in terms of (spatio-temporal) stochastic resonance. It

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44

Voss, Florian, Catherine Gloaguen, and Volker Schmidt. "CAPACITY DISTRIBUTIONS IN SPATIAL STOCHASTIC MODELS FOR TELECOMMUNICATION NETWORKS." Image Analysis & Stereology 28, no. 3 (2011): 155. http://dx.doi.org/10.5566/ias.v28.p155-163.

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We consider the stochastic subscriber line model as a spatial stochastic model for telecommunication networks and we are interested in the evaluation of the required capacities at different locations of the network in order to provide, in fine, an estimation of the cable system which has to be installed. In particular, we consider hierarchical telecommunication networks with higher–level components (HLC) and lower–level components (LLC) located on the road system underlying the network. The cable paths are modeled by shortest paths along the edge set of a stationary random tessellation, wherea

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45

Wang, Xueqian, Gang Li, and Pramod K. Varshney. "Detection of Sparse Stochastic Signals With Quantized Measurements in Sensor Networks." IEEE Transactions on Signal Processing 67, no. 8 (2019): 2210–20. http://dx.doi.org/10.1109/tsp.2019.2903034.

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46

Hayakawa, Yoshinori. "Special Section on Current Topics on Neural Networks and Stochastic Models for Information Processing." Nonlinear Theory and Its Applications, IEICE 2, no. 2 (2011): 152. http://dx.doi.org/10.1587/nolta.2.152.

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47

Ata, Barış, and Wuqin Lin. "Heavy traffic analysis of maximum pressure policies for stochastic processing networks with multiple bottlenecks." Queueing Systems 59, no. 3-4 (2008): 191–235. http://dx.doi.org/10.1007/s11134-008-9082-9.

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48

Zheng, Cheng-De, Wenlong Liang, and Zhanshan Wang. "Anti-Synchronization of Markovian Jumping Stochastic Chaotic Neural Networks with Mixed Time Delays." Circuits, Systems, and Signal Processing 33, no. 9 (2014): 2761–92. http://dx.doi.org/10.1007/s00034-014-9773-x.

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49

Musovic, Jasmin, Vlatko Lipovac, and Adriana Lipovac. "Stochastic Geometry-Based Analysis of Heterogeneous Wireless Network Spectral, Energy and Deployment Efficiency." Electronics 10, no. 7 (2021): 786. http://dx.doi.org/10.3390/electronics10070786.

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For quite a while, it has been evident that homogeneous network architectures, based on cells with a uniform radiation pattern, cannot fulfill the ever increasing demand of mobile users for capacity and service quality while still preserving spectrum and energy. However, only with the introduction of the Fourth Generation mobile communication networks to deal with the surging data traffic of multimedia applications, have smaller cells been widely used to break down service zone areas of macro base stations into multiple tiers, thus improving network performance, reducing traffic congestion, an

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50

Boitsov, V., and M. Jivitere. "The study of open-loop stochastic queuing networks with heterogeneous requests." Automatic Control and Computer Sciences 44, no. 3 (2010): 154–59. http://dx.doi.org/10.3103/s0146411610030065.

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