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Fresco, Nir. "Long-arm functional individuation of computation." Synthese 199, no. 5-6 (November 1, 2021): 13993–4016. http://dx.doi.org/10.1007/s11229-021-03407-x.
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AbstractA single physical process may often be described equally well as computing several different mathematical functions—none of which is explanatorily privileged. How, then, should the computational identity of a physical system be determined? Some computational mechanists hold that computation is individuated only by either narrow physical or functional properties. Even if some individuative role is attributed to environmental factors, it is rather limited. The computational semanticist holds that computation is individuated, at least in part, by semantic properties. She claims that the mechanistic account lacks the resources to individuate the computations performed by some systems, thereby leaving interesting cases of computational indeterminacy unaddressed. This article examines some of these views, and claims that more cases of computational indeterminacy can be addressed, if the system-environment interaction plays a greater role in individuating computations. A new, long-arm functional strategy for individuating computation is advanced.
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Olakanmi, Oladayo Olufemi, and Adedamola Dada. "An Efficient Privacy-preserving Approach for Secure Verifiable Outsourced Computing on Untrusted Platforms." International Journal of Cloud Applications and Computing 9, no. 2 (April 2019): 79–98. http://dx.doi.org/10.4018/ijcac.2019040105.
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In outsourcing computation models, weak devices (clients) increasingly rely on remote servers (workers) for data storage and computations. However, most of these servers are hackable or untrustworthy, which makes their computation questionable. Therefore, there is need for clients to validate the correctness of the results of their outsourced computations and ensure that servers learn nothing about their clients other than the outputs of their computation. In this work, an efficient privacy preservation validation approach is developed which allows clients to store and outsource their computations to servers in a semi-honest model such that servers' computational results could be validated by clients without re-computing the computation. This article employs a morphism approach for the client to efficiently perform the proof of correctness of its outsourced computation without re-computing the whole computation. A traceable pseudonym is employed by clients to enforce anonymity.
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Larsen, Brett W., and Shaul Druckmann. "Towards a more general understanding of the algorithmic utility of recurrent connections." PLOS Computational Biology 18, no. 6 (June 21, 2022): e1010227. http://dx.doi.org/10.1371/journal.pcbi.1010227.
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Lateral and recurrent connections are ubiquitous in biological neural circuits. Yet while the strong computational abilities of feedforward networks have been extensively studied, our understanding of the role and advantages of recurrent computations that might explain their prevalence remains an important open challenge. Foundational studies by Minsky and Roelfsema argued that computations that require propagation of global information for local computation to take place would particularly benefit from the sequential, parallel nature of processing in recurrent networks. Such “tag propagation” algorithms perform repeated, local propagation of information and were originally introduced in the context of detecting connectedness, a task that is challenging for feedforward networks. Here, we advance the understanding of the utility of lateral and recurrent computation by first performing a large-scale empirical study of neural architectures for the computation of connectedness to explore feedforward solutions more fully and establish robustly the importance of recurrent architectures. In addition, we highlight a tradeoff between computation time and performance and construct hybrid feedforward/recurrent models that perform well even in the presence of varying computational time limitations. We then generalize tag propagation architectures to propagating multiple interacting tags and demonstrate that these are efficient computational substrates for more general computations of connectedness by introducing and solving an abstracted biologically inspired decision-making task. Our work thus clarifies and expands the set of computational tasks that can be solved efficiently by recurrent computation, yielding hypotheses for structure in population activity that may be present in such tasks.
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MACLENNAN, BRUCE J. "EMBODIED COMPUTATION: APPLYING THE PHYSICS OF COMPUTATION TO ARTIFICIAL MORPHOGENESIS." Parallel Processing Letters 22, no. 03 (July 8, 2012): 1240013. http://dx.doi.org/10.1142/s0129626412400130.
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We discuss the problem of assembling complex physical systems that are structured from the nanoscale up through the macroscale, and argue that embryological morphogenesis provides a good model of how this can be accomplished. Morphogenesis (whether natural or artificial) is an example of embodied computation, which exploits physical processes for computational ends, or performs computations for their physical effects. Examples of embodied computation in natural morphogenesis can be found at many levels, from allosteric proteins, which perform simple embodied computations, up through cells, which act to create tissues with specific patterns, compositions, and forms. We outline a notation for describing morphogenetic programs and illustrate its use with two examples: simple diffusion and the assembly of a simple spine with attachment points for legs. While much research remains to be done — at the simulation level before we attempt physical implementations — our results to date show how we may implement the fundamental processes of morphogenesis as a practical application of embodied computation at the nano- and microscale.
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Aho, A. V. "Computation and Computational Thinking." Computer Journal 55, no. 7 (June 29, 2012): 832–35. http://dx.doi.org/10.1093/comjnl/bxs074.
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Smolensky, Paul. "Symbolic functions from neural computation." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 370, no. 1971 (July 28, 2012): 3543–69. http://dx.doi.org/10.1098/rsta.2011.0334.
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Is thought computation over ideas? Turing, and many cognitive scientists since, have assumed so, and formulated computational systems in which meaningful concepts are encoded by symbols which are the objects of computation. Cognition has been carved into parts, each a function defined over such symbols. This paper reports on a research program aimed at computing these symbolic functions without computing over the symbols. Symbols are encoded as patterns of numerical activation over multiple abstract neurons, each neuron simultaneously contributing to the encoding of multiple symbols. Computation is carried out over the numerical activation values of such neurons, which individually have no conceptual meaning. This is massively parallel numerical computation operating within a continuous computational medium. The paper presents an axiomatic framework for such a computational account of cognition, including a number of formal results. Within the framework, a class of recursive symbolic functions can be computed. Formal languages defined by symbolic rewrite rules can also be specified, the subsymbolic computations producing symbolic outputs that simultaneously display central properties of both facets of human language: universal symbolic grammatical competence and statistical, imperfect performance.
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Raussendorf, Robert. "Cohomological framework for contextual quantum computations." quantum Information and Computation 19, no. 13&14 (November 2019): 1141–70. http://dx.doi.org/10.26421/qic19.13-14-4.
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We describe a cohomological framework for measurement-based quantum computation in which symmetry plays a central role. Therein, the essential information about the computation is contained in either of two topological invariants, namely two cohomology groups. One of them applies only to deterministic quantum computations, and the other to general probabilistic ones. Those invariants characterize the computational output, and at the same time witness quantumness in the form of contextuality. In result, they give rise to fundamental algebraic structures underlying quantum computation.
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SADAKANE, Kunihiko, Noriko SUGAWARA, and Takeshi TOKUYAMA. "Quantum Computation in Computational Geometry." Interdisciplinary Information Sciences 8, no. 2 (2002): 129–36. http://dx.doi.org/10.4036/iis.2002.129.
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Miller, Douglas A., and Steven W. Zucker. "Cliques, computation, and computational tractability." Pattern Recognition 33, no. 4 (April 2000): 535–42. http://dx.doi.org/10.1016/s0031-3203(99)00070-9.
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Burgin, Mark, Eugene Eberbach, and Rao Mikkilineni. "Processing Information in the Clouds." Proceedings 47, no. 1 (May 7, 2020): 25. http://dx.doi.org/10.3390/proceedings2020047025.
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Cloud computing makes the necessary resources available to the appropriate computation to improve scaling, resiliency, and the efficiency of computations. This makes cloud computing a new paradigm for computation by upgrading its artificial intelligence (AI) to a higher order. To explore cloud computing using theoretical tools, we use cloud automata as a new model for computation. Higher-level AI requires infusing features of the human brain into AI systems such as incremental learning all the time. Consequently, we propose computational models that exhibit incremental learning without stopping (sentience). These features are inherent in reflexive Turing machines, inductive Turing machines, and limit Turing machines.
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Burgin, Mark, Eugene Eberbach, and Rao Mikkilineni. "Processing Information in the Clouds." Proceedings 47, no. 1 (May 7, 2020): 25. http://dx.doi.org/10.3390/proceedings47010025.
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Cloud computing makes the necessary resources available to the appropriate computation to improve scaling, resiliency, and the efficiency of computations. This makes cloud computing a new paradigm for computation by upgrading its artificial intelligence (AI) to a higher order. To explore cloud computing using theoretical tools, we use cloud automata as a new model for computation. Higher-level AI requires infusing features of the human brain into AI systems such as incremental learning all the time. Consequently, we propose computational models that exhibit incremental learning without stopping (sentience). These features are inherent in reflexive Turing machines, inductive Turing machines, and limit Turing machines.
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Vyas, Saurabh, Matthew D. Golub, David Sussillo, and Krishna V. Shenoy. "Computation Through Neural Population Dynamics." Annual Review of Neuroscience 43, no. 1 (July 8, 2020): 249–75. http://dx.doi.org/10.1146/annurev-neuro-092619-094115.
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Significant experimental, computational, and theoretical work has identified rich structure within the coordinated activity of interconnected neural populations. An emerging challenge now is to uncover the nature of the associated computations, how they are implemented, and what role they play in driving behavior. We term this computation through neural population dynamics. If successful, this framework will reveal general motifs of neural population activity and quantitatively describe how neural population dynamics implement computations necessary for driving goal-directed behavior. Here, we start with a mathematical primer on dynamical systems theory and analytical tools necessary to apply this perspective to experimental data. Next, we highlight some recent discoveries resulting from successful application of dynamical systems. We focus on studies spanning motor control, timing, decision-making, and working memory. Finally, we briefly discuss promising recent lines of investigation and future directions for the computation through neural population dynamics framework.
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Miłkowski, Marcin. "Morphological Computation: Nothing but Physical Computation." Entropy 20, no. 12 (December 7, 2018): 942. http://dx.doi.org/10.3390/e20120942.
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The purpose of this paper is to argue against the claim that morphological computation is substantially different from other kinds of physical computation. I show that some (but not all) purported cases of morphological computation do not count as specifically computational, and that those that do are solely physical computational systems. These latter cases are not, however, specific enough: all computational systems, not only morphological ones, may (and sometimes should) be studied in various ways, including their energy efficiency, cost, reliability, and durability. Second, I critically analyze the notion of “offloading” computation to the morphology of an agent or robot, by showing that, literally, computation is sometimes not offloaded but simply avoided. Third, I point out that while the morphology of any agent is indicative of the environment that it is adapted to, or informative about that environment, it does not follow that every agent has access to its morphology as the model of its environment.
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Hemmo, Meir, and Orly Shenker. "The Multiple-Computations Theorem and the Physics of Singling Out a Computation." Monist 105, no. 2 (March 9, 2022): 175–93. http://dx.doi.org/10.1093/monist/onab030.
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Abstract The problem of multiple-computations discovered by Hilary Putnam presents a deep difficulty for functionalism (of all sorts, computational and causal). We describe in outline why Putnam’s result, and likewise the more restricted result we call the Multiple-Computations Theorem, are in fact theorems of statistical mechanics. We show why the mere interaction of a computing system with its environment cannot single out a computation as the preferred one amongst the many computations implemented by the system. We explain why nonreductive approaches to solving the multiple-computations problem, and in particular why computational externalism, are dualistic in the sense that they imply that nonphysical facts in the environment of a computing system single out the computation. We discuss certain attempts to dissolve Putnam’s unrestricted result by appealing to systems with certain kinds of input and output states as a special case of computational externalism, and show why this approach is not workable without collapsing to behaviorism. We conclude with some remarks about the nonphysical nature of mainstream approaches to both statistical mechanics and the quantum theory of measurement with respect to the singling out of partitions and observables.
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Ghorbani, Mahdi, Mathieu Huot, Shideh Hashemian, and Amir Shaikhha. "Compiling Structured Tensor Algebra." Proceedings of the ACM on Programming Languages 7, OOPSLA2 (October 16, 2023): 204–33. http://dx.doi.org/10.1145/3622804.
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Tensor algebra is essential for data-intensive workloads in various computational domains. Computational scientists face a trade-off between the specialization degree provided by dense tensor algebra and the algorithmic efficiency that leverages the structure provided by sparse tensors. This paper presents StructTensor, a framework that symbolically computes structure at compilation time. This is enabled by Structured Tensor Unified Representation (STUR), an intermediate language that can capture tensor computations as well as their sparsity and redundancy structures. Through a mathematical view of lossless tensor computations, we show that our symbolic structure computation and the related optimizations are sound. Finally, for different tensor computation workloads and structures, we experimentally show how capturing the symbolic structure can result in outperforming state-of-the-art frameworks for both dense and sparse tensor algebra.
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Kozlov, AP. "Biological Computation and Compatibility Search in the Possibility Space as the Mechanism of Complexity Increase During Progressive Evolution." Evolutionary Bioinformatics 18 (January 2022): 117693432211106. http://dx.doi.org/10.1177/11769343221110654.
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The idea of computational processes, which take place in nature, for example, DNA computation, is discussed in the literature. DNA computation that is going on in the immunoglobulin locus of vertebrates shows how the computations in the biological possibility space could operate during evolution. We suggest that the origin of evolutionarily novel genes and genome evolution constitute the original intrinsic computation of the information about new structures in the space of unrealized biological possibilities. Due to DNA computation, the information about future structures is generated and stored in DNA as genetic information. In evolving ontogenies, search algorithms are necessary, which can search for information about evolutionary innovations and morphological novelties. We believe that such algorithms include stochastic gene expression, gene competition, and compatibility search at different levels of structural organization. We formulate the increase in complexity principle in terms of biological computation and hypothesize the possibility of in silico computing of future functions of evolutionarily novel genes.
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Huo, Chao, Peng Lv, and Anbang Sun. "Computational study on the aerodynamics of a long-shrouded contra-rotating rotor in hover." International Journal of Micro Air Vehicles 11 (January 2019): 175682931983368. http://dx.doi.org/10.1177/1756829319833686.
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This paper aims to investigate the aerodynamics including the global performance and flow characteristics of a long-shrouded contra-rotating rotor by developing a full 3D RANS computation. Through validations by current experiments on the same shrouded contra-rotating rotor, the computation using sliding mesh method and the computational zone with an extended nozzle downstream flow field effectively works; the time-averaged solution of the unsteady computation reveals that more uniform flow presents after the downstream rotor, which implies that the rear rotor rotating at opposite direction greatly compensates and reduces the wake; the unsteady computations further explore the flow field throughout the whole system, along the span and around blade tips. Complex flow patterns including the vortices and their interactions are indicated around the blade roots and tips. For further identifying rotor configurations, the rotor–rotor distance and switching two rotor speeds were studied. The computation reveals that setting the second rotor backwards decreases the wake scale but increases its intensity in the downstream nozzle zone. However, for the effect of switching speeds, computations cannot precisely solve the flow when the rear rotor under the windmill because of the upstream rotor rotating much faster than the other one. All the phenomena from computations well implement the experimental observations.
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Kim, Boram, Kwang Seok Yoon, and Hyung-Jun Kim. "GPU-Accelerated Laplace Equation Model Development Based on CUDA Fortran." Water 13, no. 23 (December 4, 2021): 3435. http://dx.doi.org/10.3390/w13233435.
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In this study, a CUDA Fortran-based GPU-accelerated Laplace equation model was developed and applied to several cases. The Laplace equation is one of the equations that can physically analyze the groundwater flows, and is an equation that can provide analytical solutions. Such a numerical model requires a large amount of data to physically regenerate the flow with high accuracy, and requires computational time. These numerical models require a large amount of data to physically reproduce the flow with high accuracy and require computational time. As a way to shorten the computation time by applying CUDA technology, large-scale parallel computations were performed on the GPU, and a program was written to reduce the number of data transfers between the CPU and GPU. A GPU consists of many ALUs specialized in graphic processing, and can perform more concurrent computations than a CPU using multiple ALUs. The computation results of the GPU-accelerated model were compared with the analytical solution of the Laplace equation to verify the accuracy. The computation results of the GPU-accelerated Laplace equation model were in good agreement with the analytical solution. As the number of grids increased, the computational time of the GPU-accelerated model gradually reduced compared to the computational time of the CPU-based Laplace equation model. As a result, the computational time of the GPU-accelerated Laplace equation model was reduced by up to about 50 times.
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HAMANN, HEIKO, and HEINZ WÖRN. "EMBODIED COMPUTATION." Parallel Processing Letters 17, no. 03 (September 2007): 287–98. http://dx.doi.org/10.1142/s0129626407003022.
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The traditional computational devices and models, such as the von Neumann architecture or the Turing machine, are strongly influenced by concepts of central control and perfection. The standard models of computation seem to cover the reality of computation only partially and lack, in particular, in the ability to describe more natural forms of computation. In this paper we propose the concept of embodied computation, a straight forward advancement of well known concepts such as amorphous computing, emergent phenomena and embodied cognitive science. Many embodied microscopic computational devices form a single macroscopic device of embodied computation. The solution to computational problems emerges from a huge amount of local interactions. The system's memory is the sum of the positional information and possibly of the internal states. Such systems are very robust and allow different methodologies to analyze computation. To back this theoretic concept some results based on simulations are given and potential benefits of this approach are discussed.
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Ernst, Udo, David Rotermund, and Klaus Pawelzik. "Efficient Computation Based on Stochastic Spikes." Neural Computation 19, no. 5 (May 2007): 1313–43. http://dx.doi.org/10.1162/neco.2007.19.5.1313.
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The speed and reliability of mammalian perception indicate that cortical computations can rely on very few action potentials per involved neuron. Together with the stochasticity of single-spike events in cortex, this appears to imply that large populations of redundant neurons are needed for rapid computations with action potentials. Here we demonstrate that very fast and precise computations can be realized also in small networks of stochastically spiking neurons. We present a generative network model for which we derive biologically plausible algorithms that perform spike-by-spike updates of the neuron's internal states and adaptation of its synaptic weights from maximizing the likelihood of the observed spike patterns. Paradigmatic computational tasks demonstrate the online performance and learning efficiency of our framework. The potential relevance of our approach as a model for cortical computation is discussed.
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Hasler, Jennifer, and Eric Black. "Physical Computing: Unifying Real Number Computation to Enable Energy Efficient Computing." Journal of Low Power Electronics and Applications 11, no. 2 (March 26, 2021): 14. http://dx.doi.org/10.3390/jlpea11020014.
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Physical computing unifies real value computing including analog, neuromorphic, optical, and quantum computing. Many real-valued techniques show improvements in energy efficiency, enable smaller area per computation, and potentially improve algorithm scaling. These physical computing techniques suffer from not having a strong computational theory to guide application development in contrast to digital computation’s deep theoretical grounding in application development. We consider the possibility of a real-valued Turing machine model, the potential computational and algorithmic opportunities of these techniques, the implications for implementation applications, and the computational complexity space arising from this model. These techniques have shown promise in increasing energy efficiency, enabling smaller area per computation, and potentially improving algorithm scaling.
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Sun, Jiameng, Binrui Zhu, Jing Qin, Jiankun Hu, and Jixin Ma. "Confidentiality-Preserving Publicly Verifiable Computation Schemes for Polynomial Evaluation and Matrix-Vector Multiplication." Security and Communication Networks 2018 (June 21, 2018): 1–15. http://dx.doi.org/10.1155/2018/5275132.
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With the development of cloud services, outsourcing computation tasks to a commercial cloud server has drawn attention of various communities, especially in the Big Data era. Public verifiability offers a flexible functionality in real circumstance where the cloud service provider (CSP) may be untrusted or some malicious users may slander the CSP on purpose. However, sometimes the computational result is sensitive and is supposed to remain undisclosed in the public verification phase, while existing works on publicly verifiable computation (PVC) fail to achieve this requirement. In this paper, we highlight the property of result confidentiality in publicly verifiable computation and present confidentiality-preserving public verifiable computation (CP-PVC) schemes for multivariate polynomial evaluation and matrix-vector multiplication, respectively. The proposed schemes work efficiently under the amortized model and, compared with previous PVC schemes for these computations, achieve confidentiality of computational results, while maintaining the property of public verifiability. The proposed schemes proved to be secure, efficient, and result-confidential. In addition, we provide the algorithms and experimental simulation to show the performance of the proposed schemes, which indicates that our proposal is also acceptable in practice.
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Miłkowski, Marcin. "Computational Mechanisms and Models of Computation." Philosophia Scientae, no. 18-3 (October 1, 2014): 215–28. http://dx.doi.org/10.4000/philosophiascientiae.1019.
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Infante Barbosa, Joaquim. "Symbolic computation in applied computational mechanics." Journal of Symbolic Computation 61-62 (February 2014): 1–2. http://dx.doi.org/10.1016/j.jsc.2013.10.004.
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Brown, B. D., and H. C. Card. "Stochastic neural computation. I. Computational elements." IEEE Transactions on Computers 50, no. 9 (2001): 891–905. http://dx.doi.org/10.1109/12.954505.
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Magnani, Lorenzo. "Eccentric Computational Embodiments: Cognitive Domestication of External Entities." Proceedings 47, no. 1 (May 15, 2020): 36. http://dx.doi.org/10.3390/proceedings2020047036.
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Eco-cognitive computationalism sees computation in context, adopting the intellectual visions advanced by the cognitive science perspectives on embodied, situated, and distributed cognition. It is in this framework that we can fruitfully study the relevance in recent computer science devoted to the simplification of cognitive and motor tasks generated in organic entities by the morphological aspects. Ignorant bodies can be cognitively “domesticated” to become useful “mimetic bodies'', which originate eccentric new computational embodiments capable of rendering an involved computation simpler and more efficient. On the basis of these considerations, we will also see how the concept of computation changes, being related to historical and contextual factors, so that the “emergence'' of new kinds of computations can be epistemologically clarified, such as the one regarding morphological computation. Finally, my presentation will introduce and discuss the concept of overcomputationalism, as intertwined with the traditional concepts of pancognitivism, paniformationalism, and pancomputationalism, seeing them in a more naturalized intellectual disposition, more appropriate to the aim of bypass ontological or metaphysical overstatements.
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Magnani, Lorenzo. "Eccentric Computational Embodiments: Cognitive Domestication of External Entities." Proceedings 47, no. 1 (May 15, 2020): 36. http://dx.doi.org/10.3390/proceedings47010036.
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Eco-cognitive computationalism sees computation in context, adopting the intellectual visions advanced by the cognitive science perspectives on embodied, situated, and distributed cognition. It is in this framework that we can fruitfully study the relevance in recent computer science devoted to the simplification of cognitive and motor tasks generated in organic entities by the morphological aspects. Ignorant bodies can be cognitively “domesticated” to become useful “mimetic bodies'', which originate eccentric new computational embodiments capable of rendering an involved computation simpler and more efficient. On the basis of these considerations, we will also see how the concept of computation changes, being related to historical and contextual factors, so that the “emergence'' of new kinds of computations can be epistemologically clarified, such as the one regarding morphological computation. Finally, my presentation will introduce and discuss the concept of overcomputationalism, as intertwined with the traditional concepts of pancognitivism, paniformationalism, and pancomputationalism, seeing them in a more naturalized intellectual disposition, more appropriate to the aim of bypass ontological or metaphysical overstatements.
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Burgin, Mark, and Rao Mikkililineni. "Seven Layers of Computation: Methodological Analysis and Mathematical Modeling." Filozofia i Nauka Zeszyt specjalny, no. 10 (May 10, 2022): 11–32. http://dx.doi.org/10.37240/fin.2022.10.zs.1.
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We live in an information society where the usage, creation, distribution, manipulation, and integration of information is a significant activity. Computations allow us to process information from various sources in various forms and use the derived knowledge in improving efficiency and resilience in our interactions with each other and with our environment. The general theory of information tells us that information to knowledge is as energy is to matter. Energy has the potential to create or modify material structures and information has the potential to create or modify knowledge structures. In this paper, we analyze computations as a vital technological phenomenon of contemporary society which allows us to process and use information. This analysis allows building classifications of computations based on their characteristics and explication of new types of computations. As a result, we extend the existing typologies of computations by delineating novel forms of information representations. While the traditional approach deals only with two dimensions of computation—symbolic and sub-symbolic, here we describe additional dimensions, namely, super-symbolic computation, hybrid computation, fused computation, blended computation, and symbiotic computation.
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Bian, Haoyu, Qichen Tan, Siyang Zhong, and Xin Zhang. "Efficient computation of broadband noise propagation using Gaussian beam tracing method." Journal of the Acoustical Society of America 151, no. 5 (May 2022): 3387–97. http://dx.doi.org/10.1121/10.0011399.
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The Gaussian beam tracing method has advantages in computing long-distance noise propagation in outdoor environments due to its high efficiency. Usually, repeated computations are required if the target sound wave is broadband or contains multiple frequencies because the method was developed in the frequency domain. This paper presents an efficient computation of broadband noise propagation using Gaussian beam tracing. The approach is based on the fact that the ray behaviors and source properties can be decoupled at high frequencies, where the wave equation is asymptotically solved. Consequently, only a single computation is needed to determine the frequency-independent ray properties, including ray-centered coordinates and beam dynamics. Then sound waves at different frequencies can be simultaneously introduced in a single computation. By separating the processes of determining the ray properties and incorporating the broadband noise source in the numerical implementation, the computational cost can be largely saved. In this work, several benchmark problems are studied, showing that the efficiency could be increased in comparison to the multiple individual computations. A computation acceleration up to 35-fold could be achieved when 200 frequency components are applied. The method is also applied to study the impact of broadband noise due to multiple drone propellers in urban environments. Applications to stochastic broadband problems are also discussed.
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Driscoll, Laura N., Krishna Shenoy, and David Sussillo. "Flexible multitask computation in recurrent networks utilizes shared dynamical motifs." Nature Neuroscience 27, no. 7 (July 2024): 1349–63. http://dx.doi.org/10.1038/s41593-024-01668-6.
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AbstractFlexible computation is a hallmark of intelligent behavior. However, little is known about how neural networks contextually reconfigure for different computations. In the present work, we identified an algorithmic neural substrate for modular computation through the study of multitasking artificial recurrent neural networks. Dynamical systems analyses revealed learned computational strategies mirroring the modular subtask structure of the training task set. Dynamical motifs, which are recurring patterns of neural activity that implement specific computations through dynamics, such as attractors, decision boundaries and rotations, were reused across tasks. For example, tasks requiring memory of a continuous circular variable repurposed the same ring attractor. We showed that dynamical motifs were implemented by clusters of units when the unit activation function was restricted to be positive. Cluster lesions caused modular performance deficits. Motifs were reconfigured for fast transfer learning after an initial phase of learning. This work establishes dynamical motifs as a fundamental unit of compositional computation, intermediate between neuron and network. As whole-brain studies simultaneously record activity from multiple specialized systems, the dynamical motif framework will guide questions about specialization and generalization.
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Cappello, Peter, and Dimitrios Mourloukos. "CX: A Scalable, Robust Network for Parallel Computing." Scientific Programming 10, no. 2 (2002): 159–71. http://dx.doi.org/10.1155/2002/598245.
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CX, a network-based computational exchange, is presented. The system's design integrates variations of ideas from other researchers, such as work stealing, non-blocking tasks, eager scheduling, and space-based coordination. The object-oriented API is simple, compact, and cleanly separates application logic from the logic that supports interprocess communication and fault tolerance. Computations, of course, run to completion in the presence of computational hosts that join and leave the ongoing computation. Such hosts, or producers, use task caching and prefetching to overlap computation with interprocessor communication. To break a potential task server bottleneck, a network of task servers is presented. Even though task servers are envisioned as reliable, the self-organizing, scalable network ofn- servers, described as asibling-connected height-balanced fat tree, tolerates a sequence ofn-1 server failures. Tasks are distributed throughout the server network via a simple "diffusion" process. CX is intended as a test bed for research on automated silent auctions, reputation services, authentication services, and bonding services. CX also provides a test bed for algorithm research into network-based parallel computation.
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Chien, Hung-Yu. "Authenticated Diffie-Hellman Key Agreement Scheme that Protects Client Anonymity and Achieves Half-Forward Secrecy." Mobile Information Systems 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/354586.
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Authenticated Diffie-Hellman key agreement (D-H key) is the de facto building block for establishing secure session keys in many security systems. Regarding the computations of authenticated D-H key agreement, the operation of modular exponentiation is the most expensive computation, which incurs a heavy loading on those clients where either their computational capacities or their batteries are limited and precious. As client’s privacy is a big concern in several e-commerce applications, it is desirable to extend authenticated D-H key agreement to protect client’s identity privacy. This paper proposes a new problem: the modified elliptic curves computational Diffie-Hellman problem (MECDHP) and proves that the MECDHP is as hard as the conventional elliptic curves computational Diffie-Hellman problem (ECDHP). Based on the MECDHP, we propose an authenticated D-H key agreement scheme which greatly improves client computational efficiency and protects client’s anonymity from outsiders. This new scheme is attractive to those applications where the clients need identity protection and lightweight computation.
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Thuruthel, Thomas George, and Fumiya Iida. "Morphological Computation and Control Complexity." IOP Conference Series: Materials Science and Engineering 1261, no. 1 (October 1, 2022): 012011. http://dx.doi.org/10.1088/1757-899x/1261/1/012011.
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Abstract Morphological computation proposes the idea that in a physical system, certain computational processes can be off-loaded to the body. However, the concept has still eluded serious theoretical quantification attempts, unlike traditional computational theory. This perspective examines the notion of morphological computation from the well established theories of traditional computation and computational complexity, drawing parallels between the two, to understand the differences and similarities. Further, we look at the quantification efforts of morphological computation and attempt to link it to the unexplored field of control complexity. We argue that the development of complexity theory for control problems is necessary to study and utilize the concept of morphological computation, if it is possible.
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Sarpeshkar, R. "Analog synthetic biology." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 372, no. 2012 (March 28, 2014): 20130110. http://dx.doi.org/10.1098/rsta.2013.0110.
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We analyse the pros and cons of analog versus digital computation in living cells. Our analysis is based on fundamental laws of noise in gene and protein expression, which set limits on the energy, time, space, molecular count and part-count resources needed to compute at a given level of precision. We conclude that analog computation is significantly more efficient in its use of resources than deterministic digital computation even at relatively high levels of precision in the cell. Based on this analysis, we conclude that synthetic biology must use analog, collective analog, probabilistic and hybrid analog–digital computational approaches; otherwise, even relatively simple synthetic computations in cells such as addition will exceed energy and molecular-count budgets. We present schematics for efficiently representing analog DNA–protein computation in cells. Analog electronic flow in subthreshold transistors and analog molecular flux in chemical reactions obey Boltzmann exponential laws of thermodynamics and are described by astoundingly similar logarithmic electrochemical potentials. Therefore, cytomorphic circuits can help to map circuit designs between electronic and biochemical domains. We review recent work that uses positive-feedback linearization circuits to architect wide-dynamic-range logarithmic analog computation in Escherichia coli using three transcription factors, nearly two orders of magnitude more efficient in parts than prior digital implementations.
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Potter, Aneirin. "044 At what resolution does the brain perform computations?" Journal of Neurology, Neurosurgery & Psychiatry 93, no. 9 (August 12, 2022): e2.239. http://dx.doi.org/10.1136/jnnp-2022-abn2.88.
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Computation is the transformation of inputs into outputs through logical operations such as AND, OR, and NOT. This literature review compares models of computation at different physiological resolutions, whole brain networks, multi-cell circuits, individual synapses and individual molecular interactions and discusses if these models might be useful for bridging between functional neuroimaging with molecular models of disease. While resolution in functional neuroimaging such as EEG, MEG, PET, and fMRI is of groups of neurons pharmacotherapy alters the brain at a molecular level. Bridging this resolution gap presents many difficulties for modellers and wider connectome projects. Sufficiently detailed models can quickly outstrip computational capacity while not including sufficient detail leads to models lacking physiological validity. This is particularly problematic when connectome projects overpromise in their capacity to understand brain disorders without basis in valid physiological models. Examples of computation at network and molecular levels suggests a lack of consensus about what resolution the brain performs computations and how these computations interact. This interaction should be a goal for further research, especially given its role in linking functional neuroimaging diagnostics and pharmacological treatments in neurology.
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Yoon, Dong-Hee, and Youngsun Han. "Parallel Power Flow Computation Trends and Applications: A Review Focusing on GPU." Energies 13, no. 9 (May 1, 2020): 2147. http://dx.doi.org/10.3390/en13092147.
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A power flow study aims to analyze a power system by obtaining the voltage and phase angle of buses inside the power system. Power flow computation basically uses a numerical method to solve a nonlinear system, which takes a certain amount of time because it may take many iterations to find the final solution. In addition, as the size and complexity of power systems increase, further computational power is required for power system study. Therefore, there have been many attempts to conduct power flow computation with large amounts of data using parallel computing to reduce the computation time. Furthermore, with recent system developments, attempts have been made to increase the speed of parallel computing using graphics processing units (GPU). In this review paper, we summarize issues related to parallel processing in power flow studies and analyze research into the performance of fast power flow computations using parallel computing methods with GPU.
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Ravani, B., and Q. J. Ge. "Computation of Spatial Displacements From Geometric Features." Journal of Mechanical Design 115, no. 1 (March 1, 1993): 95–102. http://dx.doi.org/10.1115/1.2919331.
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This paper develops the theoretical foundation for computations of spatial displacements from the simple geometric features of points, lines, planes, and their combinations. Using an oriented projective three space with a Clifford Algebra, all these three features are handled in a similar fashion. Furthermore, issues related to uniqueness of computations and minimum number of required features are discussed. It is shown that contrary to the common intuition, specification of a minimum of four points (planes) or three lines are necessary for computation of a unique displacement. Only when the sense of the orientations of these features are specified then the minimum number of required features reduces to three for points and planes and two for lines. The results, in addition to their theoretical interest in computational geometry of motion, have application in robot calibration.
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Kashiwa, Tatsuya. "Large-Scale FDTD Computation as Computational Electromagnetics." IEEJ Transactions on Fundamentals and Materials 129, no. 2 (2009): 50–53. http://dx.doi.org/10.1541/ieejfms.129.50.
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Kashiwa, Tatsuya. "Large-Scale FDTD Computation for Computational Electromagnetics." IEEJ Transactions on Fundamentals and Materials 124, no. 12 (2004): 1129–34. http://dx.doi.org/10.1541/ieejfms.124.1129.
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Dayan, P., and J. Williams. "Putting the Computation Back into Computational Modeling." Pharmacopsychiatry 39 (February 2006): 50–51. http://dx.doi.org/10.1055/s-2006-931494.
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Sathyan, Sabin, Ugur Aydin, and Anouar Belahcen. "Acoustic Noise Computation of Electrical Motors Using the Boundary Element Method." Energies 13, no. 1 (January 3, 2020): 245. http://dx.doi.org/10.3390/en13010245.
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This paper presents a numerical method and computational results for acoustic noise of electromagnetic origin generated by an induction motor. The computation of noise incorporates three levels of numerical calculation steps, combining both the finite element method and boundary element method. The role of magnetic forces in the production of acoustic noise is established in the paper by showing the magneto-mechanical and vibro-acoustic pathway of energy. The conversion of electrical energy into acoustic energy in an electrical motor through electromagnetic, mechanical, or acoustic platforms is illustrated through numerical computations of magnetic forces, mechanical deformation, and acoustic noise. The magnetic forces were computed through 2D electromagnetic finite element simulation, and the deformation of the stator due to these forces was calculated using 3D structural finite element simulation. Finally, boundary element-based computation was employed to calculate the sound pressure and sound power level in decibels. The use of the boundary element method instead of the finite element method in acoustic computation reduces the computational cost because, unlike finite element analysis, the boundary element approach does not require heavy meshing to model the air surrounding the motor.
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TRINDER, P. W., H. W. LOIDL, and R. F. POINTON. "Parallel and Distributed Haskells." Journal of Functional Programming 12, no. 4-5 (July 2002): 469–510. http://dx.doi.org/10.1017/s0956796802004343.
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Parallel and distributed languages specify computations on multiple processors and have a computation language to describe the algorithm, i.e. what to compute, and a coordination language to describe how to organise the computations across the processors. Haskell has been used as the computation language for a wide variety of parallel and distributed languages, and this paper is a comprehensive survey of implemented languages. We outline parallel and distributed language concepts and classify Haskell extensions using them. Similar example programs are used to illustrate and contrast the coordination languages, and the comparison is facilitated by the common computation language. A lazy language is not an obvious choice for parallel or distributed computation, and we address the question of why Haskell is a common functional computation language.
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Xu, Qingshan, Xiaoqing Tan, and Rui Huang. "Improved Resource State for Verifiable Blind Quantum Computation." Entropy 22, no. 9 (September 7, 2020): 996. http://dx.doi.org/10.3390/e22090996.
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Recent advances in theoretical and experimental quantum computing raise the problem of verifying the outcome of these quantum computations. The recent verification protocols using blind quantum computing are fruitful for addressing this problem. Unfortunately, all known schemes have relatively high overhead. Here we present a novel construction for the resource state of verifiable blind quantum computation. This approach achieves a better verifiability of 0.866 in the case of classical output. In addition, the number of required qubits is 2N+4cN, where N and c are the number of vertices and the maximal degree in the original computation graph, respectively. In other words, our overhead is less linear in the size of the computational scale. Finally, we utilize the method of repetition and fault-tolerant code to optimise the verifiability.
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Protsko, I. O., and D. V. Ostrovka. "ANALYSIS OF THE ERROR OF COMPUTATION FAST TRANSFORMS OF FOURIER CLASS BASED ON CYCLIC CONVOLUTIONS." Ukrainian Journal of Information Technology 2, no. 1 (2020): 52–56. http://dx.doi.org/10.23939/ujit2020.02.052.
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The features of the computational model of discrete transforms of Fourier class based on cyclic convolutions to determine the algorithmic calculation error are analyzed. Based on the approach of efficient computation of discrete transforms of Fourier class of arbitrary size N, using of a hashing array to transform a discrete basis matrix into a set of block-cyclic submatrices, the components of computational costs are considered. These components of computational costs depend on the type of transform, the size and the block-cycle structure of the transformation core. Examples of computational model and block-cyclic structure of matrices of simplified arguments of basis functions for mutually inverse discrete cosine transforms of types II, III are given. The computational model characterizes the accumulation of rounding errors at the stages of adding input data, computing cyclic convolutions, combining the results of convolutions. Discrete cyclic convolutions can be implemented using fast algorithms or a type of system that corresponds to digital filters with finite pulse characteristics. The possibility of parallel computation of the reduced number of cyclic convolutions makes the analysis of errors insensitive to rearrangement of their computations. The multiplication operations performed when computing the cyclic convolution uses a smaller number of basis coefficients equal to N/4 or N/2 depending on the size of transform. The formats of representation of real numbers in computer systems are considered, which also determine the magnitude of the computational error of transforms. The results of direct and fast computation of discrete cosine transform of type II based on cyclic convolutions with size N=58 in the format wit floating point of double precision and computation error between them are presented. The apriori process of studying the transform errors of the corresponding type and size by the method of mathematical modeling and computational experiment is approximate, which allows to predict the statistical averages of the accuracy of computing the discrete Fourier transform of arbitrary size based on cyclic convolutions.
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Cooke, Ryan A., and Suhaib A. Fahmy. "Exploring hardware accelerator offload for the Internet of Things." it - Information Technology 62, no. 5-6 (December 16, 2020): 207–14. http://dx.doi.org/10.1515/itit-2020-0017.
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AbstractThe Internet of Things is manifested through a large number of low-capability connected devices. This means that for many applications, computation must be offloaded to more capable platforms. While this has typically been cloud datacenters accessed over the Internet, this is not feasible for latency sensitive applications. In this paper we investigate the interplay between three factors that contribute to overall application latency when offloading computations in IoT applications. First, different platforms can reduce computation latency by differing amounts. Second, these platforms can be traditional server-based or emerging network-attached, which exhibit differing data ingestion latencies. Finally, where these platforms are deployed in the network has a significant impact on the network traversal latency. All these factors contributed to overall application latency, and hence the efficacy of computational offload. We show that network-attached acceleration scales better to further network locations and smaller base computation times that traditional server based approaches.
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ONDOCKO, STEFAN, JOZEF SVETLIK, TOMAS STEJSKAL, MICHAL SASALA, and LUKAS HRIVNIAK. "COMPARISON SELECTED NUMERICAL METHODS FOR THE CALCULATION INVERSE KINEMATICS OF NON-STANDARD MODULAR ROBOTIC ARM CONSISTING OF UNIQUE ROTATIONAL MODULES." MM Science Journal 2021, no. 2 (June 2, 2021): 4468–73. http://dx.doi.org/10.17973/mmsj.2021_6_2021042.
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The paper compares the most commonly used numerical methods of solving a set of nonlinear equations, especially in terms of computational speed. The methods are applied to a set of nonlinear equations that describe the forward kinematics of a non-standard robotic arm. This arm is an open-loop kinematics chain, composed of special rotary modules. A non-standard feature of the modules is the unlimited rotation around their own axis. This robotic arm consists of six such modules and, thus, has six degrees of freedom. Computations of this nonlinear set of equations are also called inverse kinematics. All computations were performed in Matlab. The same initial conditions, the computation input parameters, and the same structure of the program was used with each method. By applying the below mentioned known methods to the same set, we sought to choose a suitable computation method for the given type of mechanism.
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Bishop, John Mark. "A Cognitive Computation Fallacy? Cognition, Computations and Panpsychism." Cognitive Computation 1, no. 3 (May 30, 2009): 221–33. http://dx.doi.org/10.1007/s12559-009-9019-6.
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Fresco, Nir. "Objective Computation Versus Subjective Computation." Erkenntnis 80, no. 5 (November 15, 2014): 1031–53. http://dx.doi.org/10.1007/s10670-014-9696-8.
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Alaa Ismail, Abdalla Mostafa Elmarhomy, Abd El-Aziz Morgan, and Ashraf Mostafa Hamed. "Numerical Modeling and Geometry Enhancement of a Reactive Silencer." Journal of Advanced Research in Fluid Mechanics and Thermal Sciences 106, no. 1 (June 19, 2023): 147–57. http://dx.doi.org/10.37934/arfmts.106.1.147157.
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Internal combustion engines and blowers frequently utilize silencers to reduce exhaust noise. In the current paper, the transmission loss of reactive silencers is predicted using the plane wave decomposition method and a three-dimensional (3-D) time-domain computational fluid dynamics (CFD) approach. A mass-flow-inlet boundary condition is first used to perform a steady flow computation, which serves as an initial condition for the two subsequent unsteady flow computations. At the model's inlet, an impulse (acoustic excitation) is placed over the constant mass flow to perform the first unstable flow computation. Once the impulse has fully propagated into the silencer, the non-reflecting boundary condition (NRBC) is then added. For the scenario without acoustic excitation at the inlet, a second unsteady flow computation is performed. During the two transient computations, the time histories of the pressure and velocity at the upstream measuring points as well as the history of the pressures at the downstream measuring point are recorded. The related acoustic quantities show variations between the two unsteady flow computational findings. As a result, the transmitted sound pressure signal is just the sound pressure downstream, while the incident sound pressure signal is obtained by utilizing plane wave decomposition upstream. The transmission loss (TL) of the silencer is then calculated after the Fast Fourier Transform (FFT) converts the two sound pressure signals from the time domain to the frequency domain. The numerical calculations and the reported data are in good agreement for the published results, in addition to geometry enhancement by increasing number of holes in the cross section for muffler.
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Das, S., and J. K. Prasad. "Starting characteristics of a rectangular supersonic air-intake with cowl deflection." Aeronautical Journal 114, no. 1153 (March 2010): 177–89. http://dx.doi.org/10.1017/s0001924000003626.
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Abstract Experimental and computational investigations have been made to obtain the details of the flow field of a supersonic air-intake with different cowl deflection angles and back pressures at the exit. The flow field obtained with an inviscid computation on the basic configuration, designed for Mach 2·2, shows starting behaviour whereas computation with k-ω turbulence model and experiments indicate unstart characteristics. Both experiments and computations indicate that provision of a small angle at the cowl tip leads to start of the same intake and also improves it’s performance. Results obtained with cowl deflection shows a better performance in comparison to performance achieved with a basic intake and with a bleed of 2·8%. Sustainable back pressure could be obtained through the computations made at different back pressures for different cowl deflection angles. Overall results suggest that provision of small cowl deflection angle itself leads to improvement in performance achieved in comparison to a bleed of 2·8%, even with back pressure at the exit.