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Journal articles on the topic 'Physical Computing'

Author: Grafiati

Published: 4 June 2021

Last updated: 30 January 2023

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1

Hecht, Jeff. "Physical Limits of Computing." Computers in Physics 3, no. 4 (1989): 34. http://dx.doi.org/10.1063/1.4822857.

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2

Rosenbloom, Andrew. "Computing the physical infrastructure." netWorker 13, no. 3 (2009): 3. http://dx.doi.org/10.1145/1600303.1600304.

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3

Ananthanarayan, Swamy, and Susanne Boll. "Physical computing for children." Interactions 27, no. 3 (2020): 40–45. http://dx.doi.org/10.1145/3386235.

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4

Wolpert, David, H. Pierre Noyes, and Rolf Landauer. "Reversible Computing and Physical Law." Physics Today 45, no. 3 (1992): 98–100. http://dx.doi.org/10.1063/1.2809599.

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5

Fry, Robert. "Physical Intelligence and Thermodynamic Computing." Entropy 19, no. 3 (2017): 107. http://dx.doi.org/10.3390/e19030107.

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6

Frank, M. P. "The physical limits of computing." Computing in Science & Engineering 4, no. 3 (2002): 16–26. http://dx.doi.org/10.1109/5992.998637.

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7

PRZYBYLLA, Mareen, and Ralf ROMEIKE. "Physical Computing and its Scope - Towards a Constructionist Computer Science Curriculum with Physical Computing." Informatics in Education 13, no. 2 (2014): 225–40. http://dx.doi.org/10.15388/infedu.2014.14.

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8

Fujita, Kenichi, Syogo Yonekura, Satoshi Nishikawa, Ryuma Niiyama, and Yasuo Kuniyoshi. "Environmental and Structural Effects on Physical Reservoir Computing with Tensegrity." Journal of the Institute of Industrial Applications Engineers 6, no. 2 (2018): 92–99. http://dx.doi.org/10.12792/jiiae.6.92.

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9

Horsman, Clare, Susan Stepney, Rob C. Wagner, and Viv Kendon. "When does a physical system compute?" Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 470, no. 2169 (2014): 20140182. http://dx.doi.org/10.1098/rspa.2014.0182.

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Computing is a high-level process of a physical system. Recent interest in non-standard computing systems, including quantum and biological computers, has brought this physical basis of computing to the forefront. There has been, however, no consensus on how to tell if a given physical system is acting as a computer or not; leading to confusion over novel computational devices, and even claims that every physical event is a computation. In this paper, we introduce a formal framework that can be used to determine whether a physical system is performing a computation. We demonstrate how the abstract computational level interacts with the physical device level, in comparison with the use of mathematical models in experimental science. This powerful formulation allows a precise description of experiments, technology, computation and simulation, giving our central conclusion: physical computing is the use of a physical system to predict the outcome of an abstract evolution . We give conditions for computing, illustrated using a range of non-standard computing scenarios. The framework also covers broader computing contexts, where there is no obvious human computer user. We introduce the notion of a ‘computational entity’, and its critical role in defining when computing is taking place in physical systems.

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10

임찬 and 박찬주. "Interactive contents development utilizing physical computing." Journal of Digital Design 13, no. 1 (2013): 681–90. http://dx.doi.org/10.17280/jdd.2013.13.1.065.

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11

Nakajima, Kohei. "Physical reservoir computing—an introductory perspective." Japanese Journal of Applied Physics 59, no. 6 (2020): 060501. http://dx.doi.org/10.35848/1347-4065/ab8d4f.

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12

Aur, Dorian, Mandar Jog, and Roman R. Poznanski. "Computing by physical interaction in neurons." Journal of Integrative Neuroscience 10, no. 04 (2011): 413–22. http://dx.doi.org/10.1142/s0219635211002865.

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13

von Wangenheim, Christiane Gresse, Aldo von Wangenheim, Fernando S. Pacheco, Jean C. R. Hauck, and Miriam Nathalie F. Ferreira. "Teaching physical computing in family workshops." ACM Inroads 8, no. 1 (2017): 48–51. http://dx.doi.org/10.1145/3043950.

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14

Zak, Michail. "Physical model of immune inspired computing." Information Sciences 129, no. 1-4 (2000): 61–79. http://dx.doi.org/10.1016/s0020-0255(00)00063-3.

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15

Zhao, Feng. "Technical PerspectiveThe physical side of computing." Communications of the ACM 51, no. 7 (2008): 98. http://dx.doi.org/10.1145/1364782.1364803.

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16

Stannett, Mike. "Computing the appearance of physical reality." Applied Mathematics and Computation 219, no. 1 (2012): 54–62. http://dx.doi.org/10.1016/j.amc.2011.07.071.

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17

Peterson, Erik, and Alexander Lavin. "Physical computing for materials acceleration platforms." Matter 5, no. 11 (2022): 3586–96. http://dx.doi.org/10.1016/j.matt.2022.09.022.

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18

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 (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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19

Devine, James, Michal Moskal, Peli de Halleux, et al. "Plug-and-play Physical Computing with Jacdac." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 6, no. 3 (2022): 1–30. http://dx.doi.org/10.1145/3550317.

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Physical computing is becoming mainstream. More people than ever---from artists, makers and entrepreneurs to educators and students---are connecting microcontrollers with sensors and actuators to create new interactive devices. However, physical computing still presents many challenges and demands many skills, spanning electronics, low-level protocols, and software---road blocks that reduce participation. While USB has made connecting peripherals to a personal computing device (PC) trivial, USB components are expensive and require a PC to operate. This makes USB impractical for many physical computing scenarios where cost, size and low power operation are often important.

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20

D. Burd, Stephen, Alessandro F. Seazzu, and Christopher Conway. "Virtual Computing Laboratories: A Case Study with Comparisons to Physical Computing Laboratories." Journal of Information Technology Education: Innovations in Practice 8 (2009): 055–78. http://dx.doi.org/10.28945/173.

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21

Narang, Mrinal, Jayant Marwaha, Gurpreet Kaur, Dr Manjot Kaur Bhatia, and Ritesh Sandilya. "Quantum Computing." International Journal for Research in Applied Science and Engineering Technology 10, no. 12 (2022): 1058–63. http://dx.doi.org/10.22214/ijraset.2022.47931.

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Abstract: Quantum computing is a modern calculation method that is based on the science of quantum mechanics. These phenomena include the bizarre behavior of particles at the atomic and subatomic levels, and the way that these particles can be in multiple states simultaneously. The field of computer science is a great mix of physics, math, and information theory. This technology provides high computing power, low power consumption, and exponential speed by controlling the behavior of small physical objects, such as atoms. Atoms, electrons, photons, etc. are all elements of the physical world. We would like to introduce the basics of quantum computing, and some of the ideas behind it. This article begins with the origins of the classical computer and discusses all the improvements and transformations that have been made due to its limitations thus far, then moves on to the basic operations of quantum computing and results in quantum properties such as superposition, entanglement, and interference.

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22

Bergmann, Seth. "Using inheritance for computing with physical quantities." ACM SIGCSE Bulletin 30, no. 1 (1998): 45–47. http://dx.doi.org/10.1145/274790.273159.

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23

Kawahara, Yoshihiro, Celine Coutrix, Jason Alexander, and Albrecht Schmidt. "Physical Computing—Flexible and Shape-Changing Interfaces." IEEE Pervasive Computing 16, no. 4 (2017): 25–27. http://dx.doi.org/10.1109/mprv.2017.3971139.

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24

Horsman, D. C. "Abstraction/Representation Theory for heterotic physical computing." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 373, no. 2046 (2015): 20140224. http://dx.doi.org/10.1098/rsta.2014.0224.

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We give a rigorous framework for the interaction of physical computing devices with abstract computation. Device and program are mediated by the non-logical representation relation ; we give the conditions under which representation and device theory give rise to commuting diagrams between logical and physical domains, and the conditions for computation to occur. We give the interface of this new framework with currently existing formal methods, showing in particular its close relationship to refinement theory, and the implications for questions of meaning and reference in theoretical computer science. The case of hybrid computing is considered in detail, addressing in particular the example of an Internet-mediated social machine , and the abstraction/representation framework used to provide a formal distinction between heterotic and hybrid computing. This forms the basis for future use of the framework in formal treatments of non-standard physical computers.

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25

MATHERAT, PHILIPPE, and MARC-THIERRY JAEKEL. "Concurrent computing machines and physical space-time." Mathematical Structures in Computer Science 13, no. 5 (2003): 771–98. http://dx.doi.org/10.1017/s0960129503004067.

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26

Furness, Peter. "A physical approach to computing magnetic fields1." Geophysical Prospecting 42, no. 5 (1994): 405–16. http://dx.doi.org/10.1111/j.1365-2478.1994.tb00218.x.

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27

Stankovic, J. A., I. Lee, A. Mok, and R. Rajkumar. "Opportunities and obligations for physical computing systems." Computer 38, no. 11 (2005): 23–31. http://dx.doi.org/10.1109/mc.2005.386.

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28

Juniu, Susana. "Implementing Handheld Computing Technology in Physical Education." Journal of Physical Education, Recreation & Dance 73, no. 3 (2002): 43–48. http://dx.doi.org/10.1080/07303084.2002.10607772.

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29

Simmon, Eric, Sulayman K. Sowe, and Koji Zettsu. "Designing a Cyber-Physical Cloud Computing Architecture." IT Professional 17, no. 3 (2015): 40–45. http://dx.doi.org/10.1109/mitp.2015.51.

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30

Parsons, David, and Kathryn MacCallum. "Investigating the Classroom Environment With Physical Computing." International Journal of Mobile and Blended Learning 14, no. 4 (2022): 1–14. http://dx.doi.org/10.4018/ijmbl.315627.

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To integrate digital technologies into the curriculum, teachers must support learners to use digital tools in authentic contexts. Physical computing, which involves the use of small portable electronic devices, provides an opportunity to achieve these goals. This article reports on the initial stages of a design-based research (DBR) project that will enable students to monitor and investigate their own learning spaces, with a focus on the impacts on their own well-being, and to propose solutions to any issues that they identify. The study focuses on a series of workshops, run with staff from an educational organisation, designed to explore environmental monitoring in the classroom and identify opportunities to apply the theory of situated cognition to authentic learning in context. The article reports on the first two phases of the DBR approach, defining the project focus and understanding the problem, to propose and refine a set of five design principles. The insights gained will be used in the subsequent phases of the DBR process.

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31

Leonard, William. "Practical Computing." Practicing Anthropology 13, no. 1 (1991): 28–30. http://dx.doi.org/10.17730/praa.13.1.g517804021292218.

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Anthropometric measures such as height, weight, limb circumferences, and skinfolds are very simple yet powerful tools for evaluating physical growth and nutritional status. When applied to children under the age of 5 years, these measures provide a sensitive indicator of health and well-being among anthropological populations. In particular, such basic indices as height-for-age, weight-for-age and weight-for-height can be used to identify households and subpopulations where nutritional intervention is necessary.

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32

Richard, Gabriela T. "Employing Physical Computing in Education: How Teachers and Students Utilized Physical Computing to Develop Embodied and Tangible Learning Objects." International Journal of Technology, Knowledge, and Society 4, no. 3 (2008): 93–102. http://dx.doi.org/10.18848/1832-3669/cgp/v04i03/55887.

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33

Mousavi, Seyed. "Towards algorithm-free physical equilibrium model of computing." Quantum Information and Computation 21, no. 15&16 (2021): 1296–306. http://dx.doi.org/10.26421/qic21.15-16-3.

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Our computers today, from sophisticated servers to small smartphones, operate based on the same computing model, which requires running a sequence of discrete instructions, specified as an algorithm. This sequential computing paradigm has not yet led to a fast algorithm for an NP-complete problem despite numerous attempts over the past half a century. Unfortunately, even after the introduction of quantum mechanics to the world of computing, we still followed a similar sequential paradigm, which has not yet helped us obtain such an algorithm either. Here a completely different model of computing is proposed to replace the sequential paradigm of algorithms with inherent parallelism of physical processes. Using the proposed model, instead of writing algorithms to solve NP-complete problems, we construct physical systems whose equilibrium states correspond to the desired solutions and let them evolve to search for the solutions. The main requirements of the model are identified and quantum circuits are proposed for its potential implementation.

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34

Kumar, Dr Vinod, Er Gagandeep Raheja, and Ms Jyoti Sodhi. "CLOUD COMPUTING." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 4, no. 1 (2013): 5–7. http://dx.doi.org/10.24297/ijct.v4i1a.3025.

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Cloud computing is the delivery of computing as a service rather than a product, whereby shared resources, software, and information are provided to computers and other devices as a utility (like the electricity grid) over a network (typically the internet). Cloud computing provides computation, software, data access, and storage services that do not require end-user knowledge of the physical location and configuration of the system that delivers the services. Parallel to this concept can be drawn with the electricity grid, wherein end-users consume power without needing to understand the component devices or infrastructure required to provide the service.

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35

Noordin, Mohamad Fauzan. "Green Computing." International Journal of Green Computing 6, no. 1 (2015): 33–39. http://dx.doi.org/10.4018/ijgc.2015010103.

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Green Computing has attracted concerns in the past few decades, with the sole concern for the procedures involve in the designing, manufacturing, usage and disposal of information and communication technology (ICT) basically for environmental sustainability. The importance of human factor in green computing has not received the deserved attention. The social, moral and ethical decadence resulting from the use of ICT has reduced the quality of human in modern time. This study advocates the importance of peopleware as essential aspect of green computing which equalling demands concern. Thus the adoption of peopleware in green computing practices will invariably improve sustainability of the physical and spiritual environment.

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36

Farrell, Robert G., Jonathan Lenchner, Jeffrey O. Kephjart, et al. "Symbiotic Cognitive Computing." AI Magazine 37, no. 3 (2016): 81–93. http://dx.doi.org/10.1609/aimag.v37i3.2628.

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IBM Research is engaged in a research program in symbiotic cognitive computing to investigate how to embed cognitive computing in physical spaces. This article proposes 5 key principles of symbiotic cognitive computing. We describe how these principles are applied in a particular symbiotic cognitive computing environment and in an illustrative application.

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37

Nagano, Yuichiro. "Mesurement of stress responses using physical computing device." Stress Science Research 27 (2012): 80–87. http://dx.doi.org/10.5058/stresskagakukenkyu.27.80.

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38

Bakus, Jan, Laurent Bernardin, Jürgen Gerhard, Kaska Kowalska, Mathieu Léger, and Allan Wittkopf. "High-Level Physical Modeling Description and Symbolic Computing." IFAC Proceedings Volumes 41, no. 2 (2008): 1054–55. http://dx.doi.org/10.3182/20080706-5-kr-1001.00180.

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39

Kan, Shaohua, Kohei Nakajima, Tetsuya Asai, and Megumi Akai‐Kasaya. "Physical Implementation of Reservoir Computing through Electrochemical Reaction." Advanced Science 9, no. 6 (2021): 2104076. http://dx.doi.org/10.1002/advs.202104076.

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40

Barnaghi, Payam, Amit Sheth, Vivek Singh, and Manfred Hauswirth. "Physical-Cyber-Social Computing: Looking Back, Looking Forward." IEEE Internet Computing 19, no. 3 (2015): 7–11. http://dx.doi.org/10.1109/mic.2015.65.

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41

Tanaka, Gouhei, Toshiyuki Yamane, Jean Benoit Héroux, et al. "Recent advances in physical reservoir computing: A review." Neural Networks 115 (July 2019): 100–123. http://dx.doi.org/10.1016/j.neunet.2019.03.005.

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42

Edwards, Morfydd G., and Bernard E. Weller. "Compulab—Computing for the physical sciences teaching laboratory." Computers & Education 10, no. 2 (1986): 307–13. http://dx.doi.org/10.1016/0360-1315(86)90033-3.

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43

Zhou, Zude, Jianmin Hu, Quan Liu, Ping Lou, Junwei Yan, and Wenfeng Li. "Fog Computing-Based Cyber-Physical Machine Tool System." IEEE Access 6 (2018): 44580–90. http://dx.doi.org/10.1109/access.2018.2863258.

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44

Atif, Muhammad, Siddique Latif, Rizwan Ahmad, et al. "Soft Computing Techniques for Dependable Cyber-Physical Systems." IEEE Access 7 (2019): 72030–49. http://dx.doi.org/10.1109/access.2019.2920317.

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45

Berzowska, Joanna. "Personal technologies: memory and intimacy through physical computing." AI & SOCIETY 20, no. 4 (2006): 446–61. http://dx.doi.org/10.1007/s00146-006-0033-x.

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46

Guo, Song, Zhen Liu, and Peng Li. "Editorial: Big Data and Cyber-Physical-Social Computing." Mobile Networks and Applications 24, no. 4 (2019): 1346–47. http://dx.doi.org/10.1007/s11036-019-01307-6.

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47

McAdam, Rohan. "Computing Through Movement." Proceedings of the AAAI Conference on Human Computation and Crowdsourcing 2 (September 5, 2014): 46–47. http://dx.doi.org/10.1609/hcomp.v2i1.13178.

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One human capability that seems to have received little attention from researchers in human computation so far is our ability to understand and manipulate the complex dynamics of physical movement. This research investigates the use of human motor learning as a mechanism for exploration and problem solving for nonlinear dynamical systems. This paper illustrates the approach with an example from the study of sustainable economic growth.

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48

Alakbarov, Rashid, and Mammad Hashimov. "Fog computing technology application in cyber-physical systems and analysis of cybersecurity problems." Problems of Information Society 13, no. 2 (2022): 23–29. http://dx.doi.org/10.25045/jpis.v13.i2.03.

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New requirements for modern technologies have become a driving force in the development of information technology. New distributed computing systems are required to handle a large data flow generated by the application of the Internet of Things and to ensure their efficient processing. Although cloud computing is an effective technology for processing and storing data generated in a networked environment, it has complications with the real time transmission of large amounts of data due to the low bandwidth of network. To speed up the data processing, fog computing systems have been widely used in recent years. Fog counting systems are one of the proposed solutions for working with IoT devices. Because it can meet the computing needs of multiple devices connected to the network. In these systems, the data is processed at computing nodes located near the data generating devices, which reduces the bandwidth complications of the network channel. In this regard, this article considers the application of fog computing technology in cyber-physical systems. It analyzes the fog technology architecture and its advantages over cloud computing. Cyber security problems arising when using fog technology in cyber-physical systems are analyzed and available protection methods partially solving them are highlighted.

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49

MASCARI, G. F. "TOWARDS NONCOMMUTATIVE COMPUTING." International Journal of Modern Physics B 14, no. 22n23 (2000): 2451–54. http://dx.doi.org/10.1142/s0217979200001965.

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This paper presents first steps of an approach to quantum information processing in the framework of higher category theory from a noncommutative mathematics perspective. The aim is to provide a unifying theory for the structure and dynamics of composite quantum information processing systems, such that states, evolution, entanglement, decoherence are modeled by abstract categorical constructions and vice versa new mathematical structures arising from higher dimensional algebra could be "tested" as computational schemes and possibly realized by physical experiments.

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50

Wang, Feng. "Enlightenment of Physical Education Teaching Experiment Based on Cloud Computing to the Current Physical Education Reform." Scientific Programming 2021 (October 18, 2021): 1–11. http://dx.doi.org/10.1155/2021/6607539.

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Our country has a large land area, and the development of physical education is not balanced. In daily teaching activities, teachers and students use computers and networks to teach, which generates massive amounts of data. Schools are limited by funds and cannot meet the growing demand for storage of teaching resources. It is also unable to realize the sharing of teaching resources. In order to solve the problems existing in the existing education and teaching platform of the school, especially in the teaching reform, to meet the requirements of all parties facing physical education, the concept of cloud computing was proposed, and the services and methods provided by the cloud computing-based teaching resource platform were discussed. Through the questionnaire survey method of college students and teachers, statistical methods and logical analysis methods were used to analyze the data collected in the questionnaire. Summary and analysis are as follows. The survey results show that more than 50% of the people are dissatisfied with the current physical education and believe that it has not played its due role, and more than 70% of the people agree with the reform of physical education. The experimental results also show that interesting and diverse physical education courses can attract students to participate and increase their interest. From the overall survey results, the problem of college physical education courses is more serious, and it is urgent to optimize teaching from the cloud computing level. On the one hand, it is necessary to improve the relevant cloud computing and other technical platform facilities; on the other hand, it is necessary to improve the teaching level of teachers and change the current educational concept to make it livelier and more interesting.

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