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Journal articles on the topic 'Intelligence reconfigurable'

Author: Grafiati

Published: 17 September 2022

Last updated: 18 September 2022

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1

Ivchenko, Valeriy, Petr Krug, Ekaterina Matyukhina, and Sergey Pavelyev. "Mars-500 Program Space-Based Mobile Robot “Turist”." Applied Mechanics and Materials 789-790 (September 2015): 742–46. http://dx.doi.org/10.4028/www.scientific.net/amm.789-790.742.

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The problems of design and implementation of remotely reconfigurable intelligence for space-based robotic systems and, specifically, mobile robots are highlighted. The classification of reconfiguration technologies, the specifications of remote reconfiguration, the functional structure of remotely reconfigurable intelligence are described. The space-based mobile robot-explorer "Turist" is presented.

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2

Zhang, Hai-Tian, Tae Joon Park, A. N. M. Nafiul Islam, Dat S. J. Tran, Sukriti Manna, Qi Wang, Sandip Mondal, et al. "Reconfigurable perovskite nickelate electronics for artificial intelligence." Science 375, no. 6580 (February 4, 2022): 533–39. http://dx.doi.org/10.1126/science.abj7943.

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Reconfigurable devices offer the ability to program electronic circuits on demand. In this work, we demonstrated on-demand creation of artificial neurons, synapses, and memory capacitors in post-fabricated perovskite NdNiO 3 devices that can be simply reconfigured for a specific purpose by single-shot electric pulses. The sensitivity of electronic properties of perovskite nickelates to the local distribution of hydrogen ions enabled these results. With experimental data from our memory capacitors, simulation results of a reservoir computing framework showed excellent performance for tasks such as digit recognition and classification of electrocardiogram heartbeat activity. Using our reconfigurable artificial neurons and synapses, simulated dynamic networks outperformed static networks for incremental learning scenarios. The ability to fashion the building blocks of brain-inspired computers on demand opens up new directions in adaptive networks.

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Crawford, Lara S., Minh Binh Do, Wheeler S. Ruml, Haitham Hindi, Craig Eldershaw, Rong Zhou, Lukas Kuhn, et al. "On-Line Reconfigurable Machines." AI Magazine 34, no. 3 (September 15, 2013): 73–88. http://dx.doi.org/10.1609/aimag.v34i3.2387.

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A recent trend in intelligent machines and manufacturing has been toward reconfigurable manufacturing systems, which move away from the idea of a fixed factory line executing an unchanging set of operations, and toward the goal of an adaptable factory structure. The logical next challenge in this area is that of on-line reconfigurability. With this capability, machines can reconfigure while running, enable or disable capabilities in real time, and respond quickly to changes in the system or the environment (including faults). We propose an approach to achieving on-line reconfigurability based on a high level of system modularity supported by integrated, model-based planning and control software. Our software capitalizes on many advanced techniques from the artificial intelligence research community, particularly in model-based domain-independent planning and scheduling, heuristic search, and temporal resource reasoning. We describe the implementation of this design in a prototype highly modular, parallel printing system.

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Na, Jin Hee, Ho Seok Ahn, Myoung Soo Park, and Jin Young Choi. "Development of Reconfigurable and Evolvable Architecture for Intelligence Implement." Journal of Fuzzy Logic and Intelligent Systems 15, no. 7 (December 1, 2005): 823–27. http://dx.doi.org/10.5391/jkiis.2005.15.7.823.

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5

Goyal, K. K., P. K. Jain, and M. Jain. "Applying Swarm Intelligence to Design the Reconfigurable Flow Lines." International Journal of Simulation Modelling 12, no. 1 (March 15, 2013): 17–26. http://dx.doi.org/10.2507/ijsimm12(1)2.220.

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6

Khan, Arsalan H., Zhang Weiguo, Shi Jingping, and Zeashan H. Khan. "Optimized Reconfigurable Modular Flight Control Design using Swarm Intelligence." Procedia Engineering 24 (2011): 621–28. http://dx.doi.org/10.1016/j.proeng.2011.11.2706.

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7

Wei, Shaojun. "Reconfigurable computing: a promising microchip architecture for artificial intelligence." Journal of Semiconductors 41, no. 2 (February 2020): 020301. http://dx.doi.org/10.1088/1674-4926/41/2/020301.

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8

LIU, YUBIN, RUOPENG WEI, HUIJUAN DONG, YANHE ZHU, and JIE ZHAO. "A DESIGNATION OF MODULAR MOBILE RECONFIGURABLE PLATFORM SYSTEM." Journal of Mechanics in Medicine and Biology 20, no. 09 (September 16, 2020): 2040006. http://dx.doi.org/10.1142/s0219519420400060.

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Mobile robots working in special environment have to adapt for unknown and complex environment characteristics, so high mobility, functional versatility and robustness of mobile robots are required. Different from specialized robot designed for single function in single environment, single unit of modular reconfigurable robots has simple mechanical structure, flexible movement and maneuverability; meanwhile, the combination of multiple units has flexible and versatile configuration, combined with distributed control and swarm intelligence algorithm to gain environmental adaptability and functional versatility of the entire reconfigurable robot system. Single unit of modular mobile reconfigurable robots could complete lightweight tasks such as transporting medicines, distributing and accompanying nurses. Meanwhile, the combination of multiple units could complete heavyweight tasks such as transporting patients and large medical equipment. Modular mobile reconfigurable robot system has broad application prospects in the field of medical auxiliary robots.

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9

Zhao, Tao, Bin Zi, Sen Qian, Zeqiang Yin, and Dan Zhang. "Typical configuration analysis of a modular reconfigurable cable-driven parallel robot." International Journal of Advanced Robotic Systems 16, no. 2 (March 1, 2019): 172988141983475. http://dx.doi.org/10.1177/1729881419834756.

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To obtain better flexibility and multifunction in varying practical applications, several typical configurations of a modular reconfigurable cable-driven parallel robot are analyzed in this article. The spatial topology of the modular reconfigurable cable-driven parallel robot can be reconfigured by manually detaching or attaching the different number of modular branches as well as changing the connection points on the end-effector to satisfy diverse task requirements. The structure design of the modular reconfigurable cable-driven parallel robot is depicted in detail, including the design methodology, mechanical description, and control architecture. The inverse kinematics and dynamics of the modular reconfigurable cable-driven parallel robot considering diverse configurations are derived according to the vector closed rule and Lagrange method, respectively. The numerical simulation and related experiments of a typical configuration are achieved and analyzed. The results verify the effectiveness and feasibility of the inverse kinematics and dynamics models for the modular reconfigurable cable-driven parallel robot.

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10

Ignatov, Alexander, Valeriy Ivchenko, Petr Krug, Ekaterina Matyukhina, and Sergey Pavelyev. "The Technologies for Remote Reconfiguration of Artificial Intelligence of Robotic Systems in Case of Mission or Driving Conditions Change." Applied Mechanics and Materials 851 (August 2016): 477–83. http://dx.doi.org/10.4028/www.scientific.net/amm.851.477.

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This report considers the creation of a controller intended for reconfiguring the artificial intelligence of robotic vehicles. The functional structure of hardware-reconfigurable digital module for intellectual control of robotic vehicles is proposed and further interaction between its functional modules and remote support center in different situations requiring reconfiguration is concerned. The procedures of self-check and self-testing of the hardware-reconfigurable digital module for intellectual control of mobile space-based robots are described, which are necessary to ensure reliability of reconfiguration.

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11

Tang, Li, Yoram Koren, Derek M. Yip-Hoi, and Wencai Wang. "Computer-Aided Reconfiguration Planning: An Artificial Intelligence-Based Approach." Journal of Computing and Information Science in Engineering 6, no. 3 (February 20, 2006): 230–40. http://dx.doi.org/10.1115/1.2218369.

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The manufacturing industry today faces a highly volatile market in which manufacturing systems must be capable of responding rapidly to market changes while fully exploiting existing resources. Reconfigurable manufacturing systems (RMS) are designed for this purpose and are gradually being deployed by many mid-to-large volume manufacturers. The advent of RMS has given rise to a challenging problem, namely, how to economically and efficiently reconfigure a manufacturing system and the reconfigurable hardware within it so that the system can meet new requirements. This paper presents a solution to this problem that models the reconfigurability of a RMS as a network of potential activities and configurations to which a shortest path graph-searching strategy is applied. Two approaches using the A* algorithm and a genetic algorithm are employed to perform this search for the reconfiguration plan and reconfigured system that best satisfies the new performance goals. This search engine is implemented within an AI-based computer-aided reconfiguration planning (CARP) framework, which is designed to assist manufacturing engineers in making reconfiguration planning decisions. Two planning problems serve as examples to prove the effectiveness of the CARP framework.

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12

Fei, Yanqiong. "Docking Design of Self-Reconfigurable Robot." International Journal of Advanced Robotic Systems 8, no. 5 (January 1, 2011): 70. http://dx.doi.org/10.5772/45709.

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Docking design of self-reconfigurable robots is studied. Firstly, the self-reconfigurable robot is presented. Its basic module is designed, which is composed of a central cube and six rotary arms. Then, the novel docking mechanism of each module is designed. It is critical for the self-reconfigurable robot to discard any faulty modules for the self-repairing actions. The docking process is analyzed with the geometric method. The docking forces between two modules are described with the static equilibrium condition and the small motion's method. It shows that the reliability of the connection will be increased when the module's weight G is increased. It is important to finish the docking action in the self-reconfigurable robot. At last, a simulation of six-module and an experiment of three-module show that the modules can finish the docking process effectively.

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13

Chia-Hui Chang, Harianto Siek, Jiann-Jyh Lu, Chun-Nan Hsu, and Jen-Jie Chiou. "Reconfigurable Web wrapper agents." IEEE Intelligent Systems 18, no. 5 (September 2003): 34–40. http://dx.doi.org/10.1109/mis.2003.1234767.

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14

Bharathi, N., and P. Neelamegam. "A Reconfigurable Framework for Cloud Computing Architecture." Journal of Artificial Intelligence 6, no. 1 (December 15, 2012): 117–20. http://dx.doi.org/10.3923/jai.2013.117.120.

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15

Østergaard, Esben Hallundbæk, Kristian Kassow, Richard Beck, and Henrik Hautop Lund. "Design of the ATRON lattice-based self-reconfigurable robot." Autonomous Robots 21, no. 2 (August 25, 2006): 165–83. http://dx.doi.org/10.1007/s10514-006-8546-1.

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16

Zhang, Shun, Muye Li, Mengnan Jian, Yajun Zhao, and Feifei Gao. "AIRIS: Artificial intelligence enhanced signal processing in reconfigurable intelligent surface communications." China Communications 18, no. 7 (July 2021): 158–71. http://dx.doi.org/10.23919/jcc.2021.07.013.

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17

Sprowitz, Alexander, Soha Pouya, Stephane Bonardi, Jesse Van Den Kieboom, Rico Mockel, Aude Billard, Pierre Dillenbourg, and Auke Jan Ijspeert. "Roombots: Reconfigurable Robots for Adaptive Furniture." IEEE Computational Intelligence Magazine 5, no. 3 (August 2010): 20–32. http://dx.doi.org/10.1109/mci.2010.937320.

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18

Nedjah, Nadia, and Luiza de Macedo Mourelle. "Reconfigurable hardware for neural networks: binary versus stochastic." Neural Computing and Applications 16, no. 3 (March 13, 2007): 249–55. http://dx.doi.org/10.1007/s00521-007-0086-x.

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19

Kostadinov, Atanas N., and Guennadi A. Kouzaev. "A Novel Processor for Artificial Intelligence Acceleration." WSEAS TRANSACTIONS ON CIRCUITS AND SYSTEMS 21 (July 1, 2022): 125–41. http://dx.doi.org/10.37394/23201.2022.21.14.

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A variable predicate logic processor (VPLP) is proposed for artificial intelligence (AI), robotics, computer-aided medicine, electronic security, and other applications. The development is realized as an accelerating unit in AI computing machines. The difference from known designs, the datapath of this processor consists of universal gates changing on-the-fly their logical styles-subsets of predicate logic according to the data type and implemented instructions. In this paper, the processor’s reconfigurable gates and the main units are proposed, designed, modeled, and verified using a Field-Programmable Gate Array (FPGA) board and corresponding computer-aided design (CAD) tool. The implemented processor confirmed its reconfigurability on-the-fly performing testing codes. This processor is interesting in accelerating AI computing, molecular and quantum calculations in science, cryptography, computer-aided medicine, robotics, etc.

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20

Chang, J. Morris, and C. Dan Lo. "FPGA-based reconfigurable computing." Microprocessors and Microsystems 30, no. 6 (September 2006): 281–82. http://dx.doi.org/10.1016/j.micpro.2006.04.003.

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21

Fei, Yanqiong, Yueliang Zhu, and Ping Xia. "Analysis on Self-Morphing Process of Self-Reconfigurable Modular Robot." International Journal of Advanced Robotic Systems 6, no. 3 (January 1, 2009): 23. http://dx.doi.org/10.5772/7232.

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The self-reconfigurable modular robot consists of many identical modules. By connecting to/disconnecting from other modules, the whole structure of the robot can transform into arbitrary other configurations. First, the lattice-type self-reconfigurable modular robot is proposed and its disconnected/connected mechanism is analyzed, which can finish self-morphing action. Second, the basic configuration of the module is analyzed with the eigenvector matrix. The motion rules are proposed. Third, the possible motion space is described with the geometric feature of modules which is effective for performing the self-morphing process. Then, the self-morphing motion process is described with the driving function and the adjacency matrix which is useful to solve the computation problem and optimize the motion paths of the robot during the self-reconfigurable morphing process. Final, an experiment of three-module motion and a simulation of multi-module's self-morphing process are shown to prove that the above analyses are effective.

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22

Kujoth, Richard B., Chi-Wei Wang, Jeffrey J. Cook, Derek B. Gottlieb, and Nicholas P. Carter. "A wire delay-tolerant reconfigurable unit for a clustered programmable-reconfigurable processor." Microprocessors and Microsystems 31, no. 2 (March 2007): 146–59. http://dx.doi.org/10.1016/j.micpro.2006.03.001.

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23

Roehr, Thomas M. "A Constraint-based Mission Planning Approach for Reconfigurable Multi-Robot Systems." Inteligencia Artificial 21, no. 62 (September 7, 2018): 25. http://dx.doi.org/10.4114/intartif.vol21iss62pp25-39.

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The application of reconfigurable multi-robot systems introduces additional degrees of freedom to design robotic missions compared to classical multi-robot systems. To allow for autonomous operation of such systems, planning approaches have to be investigated that cannot only cope with the combinatorial challenge arising from the increased flexibility of modular systems, but also exploit this flexibility to improve for example the safety of operation. While the problem originates from the domain of robotics it is of general nature and significantly intersects with operations research. This paper suggests a constraint-based mission planning approach, and presents a set of revised definitions for reconfigurable multi-robot systems including the representation of the planning problem using spatially and temporally qualified resource constraints. Planning is performed using a multi-stage approach and a combined use of knowledge-based reasoning, constraint-based programming and integer linear programming. The paper concludes with the illustration of the solution of a planned example mission.

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24

Sundari, B. Bala Tripura. "Mapping Multi-Loop Nest Algorithms on to Reconfigurable Architecture." Journal of Artificial Intelligence 5, no. 4 (September 15, 2012): 142–51. http://dx.doi.org/10.3923/jai.2012.142.151.

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25

Yu, Minjing, Yong-Jin Liu, Yulin Zhang, Guozhen Zhao, Chun Yu, and Yuanchun Shi. "Interactions With Reconfigurable Modular Robots Enhance Spatial Reasoning Performance." IEEE Transactions on Cognitive and Developmental Systems 12, no. 2 (June 2020): 300–310. http://dx.doi.org/10.1109/tcds.2019.2914162.

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26

Shevelev, S. S. "RECONFIGURABLE COMPUTING MODULAR SYSTEM." Radio Electronics, Computer Science, Control 1, no. 1 (March 31, 2021): 194–207. http://dx.doi.org/10.15588/1607-3274-2021-1-19.

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Context. Modern general purpose computers are capable of implementing any algorithm, but when solving certain problems in terms of processing speed they cannot compete with specialized computing modules. Specialized devices have high performance, effectively solve the problems of processing arrays, artificial intelligence tasks, and are used as control devices. The use of specialized microprocessor modules that implement the processing of character strings, logical and numerical values, represented as integers and real numbers, makes it possible to increase the speed of performing arithmetic operations by using parallelism in data processing. Objective. To develop principles for constructing microprocessor modules for a modular computing system with a reconfigurable structure, an arithmetic-symbolic processor, specialized computing devices, switching systems capable of configuring microprocessors and specialized computing modules into a multi-pipeline structure to increase the speed of performing arithmetic and logical operations, high-speed design algorithms specialized processors-accelerators of symbol processing. To develop algorithms, structural and functional diagrams of specialized mathematical modules that perform arithmetic operations in direct codes on neural-like elements and systems for decentralized control of the operation of blocks. Method. An information graph of the computational process of a modular system with a reconstructed structure has been built. Structural and functional diagrams, algorithms that implement the construction of specialized modules for performing arithmetic and logical operations, search operations and functions for replacing occurrences in processed words have been developed. Software has been developed for simulating the operation of an arithmetic-symbolic processor, specialized computing modules, and switching systems. Results. A block diagram of a reconfigurable computing modular system has been developed, which consists of compatible functional modules, it is capable of static and dynamic reconfiguration, has a parallel structure for connecting the processor and computing modules through the use of interface channels. The system consists of an arithmetic-symbolic processor, specialized computing modules and switching systems, performs specific tasks of symbolic information processing, arithmetic and logical operations. Conclusions. The architecture of reconfigurable computing systems can change dynamically during their operation. It becomes possible to adapt the architecture of a computing system to the structure of the problem being solved, to create problem-oriented computers, the structure of which corresponds to the structure of the problem being solved. As the main computing element in reconfigurable computing systems, not universal microprocessors are used, but programmable logic integrated circuits, which are combined using high-speed interfaces into a single computing field. Reconfigurable multipipeline computing systems based on fields are an effective tool for solving streaming information processing and control problems.

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27

Krysander, Mattias, Fredrik Heintz, Jacob Roll, and Erik Frisk. "FlexDx: A reconfigurable diagnosis framework." Engineering Applications of Artificial Intelligence 23, no. 8 (December 2010): 1303–13. http://dx.doi.org/10.1016/j.engappai.2010.01.004.

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28

Lee, Gareth, and George Milne. "Programming paradigms for reconfigurable computing." Microprocessors and Microsystems 29, no. 10 (December 2005): 435–50. http://dx.doi.org/10.1016/j.micpro.2005.01.002.

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29

Chang, J. Morris, and C. Dan Lo. "FPGA-based reconfigurable computing II." Microprocessors and Microsystems 31, no. 2 (March 2007): iv—v. http://dx.doi.org/10.1016/j.micpro.2007.01.003.

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30

Lo, Chia-Tien Dan, and J. Morris Chang. "FPGA-based reconfigurable computing III." Microprocessors and Microsystems 31, no. 8 (December 2007): 475–76. http://dx.doi.org/10.1016/j.micpro.2007.02.002.

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31

Borecký, Jaroslav, Martin Kohlík, and Hana Kubátová. "Parity driven reconfigurable duplex system." Microprocessors and Microsystems 52 (July 2017): 251–60. http://dx.doi.org/10.1016/j.micpro.2017.06.015.

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32

Anghelescu, Petre, Silviu Ionita, and Vasile Gabriel Iana. "HIGH-SPEED PCA ENCRYPTION ALGORITHM USING RECONFIGURABLE COMPUTING." Cybernetics and Systems 44, no. 4 (May 19, 2013): 285–304. http://dx.doi.org/10.1080/01969722.2013.783375.

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33

Tang, Hao, Di Li, Jiafu Wan, Muhammad Imran, and Muhammad Shoaib. "A Reconfigurable Method for Intelligent Manufacturing Based on Industrial Cloud and Edge Intelligence." IEEE Internet of Things Journal 7, no. 5 (May 2020): 4248–59. http://dx.doi.org/10.1109/jiot.2019.2950048.

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34

Pal, Rajesh Kumar, Kolin Paul, and Sanjiva Prasad. "ReKonf: Dynamically reconfigurable multiCore architecture." Journal of Parallel and Distributed Computing 74, no. 11 (November 2014): 3071–86. http://dx.doi.org/10.1016/j.jpdc.2014.05.007.

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35

Eshaghian, Mary M., and Lili Hai. "An Optically Interconnected Reconfigurable Mesh." Journal of Parallel and Distributed Computing 61, no. 6 (June 2001): 737–47. http://dx.doi.org/10.1006/jpdc.2001.1704.

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36

Kuo, Sy-Yen, and W. Kent Fuchs. "Reconfigurable cube-connected cycles architectures." Journal of Parallel and Distributed Computing 9, no. 1 (May 1990): 1–10. http://dx.doi.org/10.1016/0743-7315(90)90106-y.

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37

DeHon, André, Randy Huang, and John Wawrzynek. "Stochastic spatial routing for reconfigurable networks." Microprocessors and Microsystems 30, no. 6 (September 2006): 301–18. http://dx.doi.org/10.1016/j.micpro.2006.02.003.

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38

Beck, Antonio Carlos Schneider, Mateus Beck Rutzig, and Luigi Carro. "A transparent and adaptive reconfigurable system." Microprocessors and Microsystems 38, no. 5 (July 2014): 509–24. http://dx.doi.org/10.1016/j.micpro.2014.03.004.

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39

Bouganis, Christos-Savvas, Marek Gorgon, and Vanderlei Bonato. "Special issue on applied reconfigurable computing." Microprocessors and Microsystems 52 (July 2017): 1. http://dx.doi.org/10.1016/j.micpro.2017.05.010.

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40

Matsusaka, Yosuke, Hideki Asoh, Isao Hara, and Futoshi Asano. "Specification and Implementation of Open Source Software Suite for Realizing Communication Intelligence." Journal of Robotics and Mechatronics 24, no. 1 (February 20, 2012): 86–94. http://dx.doi.org/10.20965/jrm.2012.p0086.

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We are presently developing a set of software called the Open Source Software Suite for Human Robot Interaction (OpenHRI). The OpenHRI has the following features: It is implemented on RT-Component, an Object Management Group (OMG) compliant robot technology component specification that can be easily integrated into any robot system. It can perform various functions, from audio signal processing to dialog management, in a uniform and reconfigurable manner. It not only implements each required function of components but also defines a meta-level specification to enable the developer to verify whether the structural design of components is correct. In this paper, we introduce the implementation of the OpenHRI, present the architectural design of the system, and provide examples of applications.

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41

Choi, Yoon-Hwa. "Reconfigurable VLSI/WSI multipipelines." Parallel Computing 17, no. 8 (October 1991): 941–52. http://dx.doi.org/10.1016/s0167-8191(05)80077-8.

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42

Bakkar, Mostafa, Santiago Bogarra, Felipe Córcoles, Ahmed Aboelhassan, Shuo Wang, and Javier Iglesias. "Artificial Intelligence-Based Protection for Smart Grids." Energies 15, no. 13 (July 5, 2022): 4933. http://dx.doi.org/10.3390/en15134933.

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Lately, adequate protection strategies need to be developed when Microgrids (MGs) are connected to smart grids to prevent undesirable tripping. Conventional relay settings need to be adapted to changes in Distributed Generator (DG) penetrations or grid reconfigurations, which is a complicated task that can be solved efficiently using Artificial Intelligence (AI)-based protection. This paper compares and validates the difference between conventional protection (overcurrent and differential) strategies and a new strategy based on Artificial Neural Networks (ANNs), which have been shown as adequate protection, especially with reconfigurable smart grids. In addition, the limitations of the conventional protections are discussed. The AI protection is employed through the communication between all Protective Devices (PDs) in the grid, and a backup strategy that employs the communication among the PDs in the same line. This paper goes a step further to validate the protection strategies based on simulations using the MATLABTM platform and experimental results using a scaled grid. The AI-based protection method gave the best solution as it can be adapted for different grids with high accuracy and faster response than conventional protection, and without the need to change the protection settings. The scaled grid was designed for the smart grid to advocate the behavior of the protection strategies experimentally for both conventional and AI-based protections.

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43

Sun, Hanxu, Mingzhe Li, Jingzhou Song, and Yun Wang. "Research on self-reconfiguration strategy of modular spherical robot." International Journal of Advanced Robotic Systems 19, no. 2 (March 1, 2022): 172988062210816. http://dx.doi.org/10.1177/17298806221081665.

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Self-reconfigurable robot is a complex system composed of multiple modular robots. Aiming at high efficiency and low energy consumption of self-reconfigurable robot configuration transformation, a self-reconfiguration strategy based on module mapping of the common parts is proposed. This strategy describes the configuration of the robot in the form of a graph, and a method to determine the central node of configuration is proposed. The central node module as the starting node for comparison of different configurations, and the common part between the two configurations is reserved. Then the module closest to the target module is searched, the target configuration is reconfigured from the inside to the outside with the minimum energy consumption constraint. Finally, based on the experiment results, compared with other self-reconfiguration strategies, the proposed self-reconfiguration strategy reduces the times of reconfiguration operations and improves the reconfiguration efficiency.

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44

Sueyoshi, T. "Special Section on Reconfigurable Systems." IEICE Transactions on Information and Systems E90-D, no. 12 (December 1, 2007): 1903–4. http://dx.doi.org/10.1093/ietisy/e90-d.12.1903.

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45

Yang, Le, Zhigang Zeng, and Yi Huang. "An Associative-Memory-Based Reconfigurable Memristive Neuromorphic System With Synchronous Weight Training." IEEE Transactions on Cognitive and Developmental Systems 12, no. 3 (September 2020): 529–40. http://dx.doi.org/10.1109/tcds.2019.2932179.

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46

Christensen, David Johan, Jason Campbell, and Kasper Stoy. "Anatomy-based organization of morphology and control in self-reconfigurable modular robots." Neural Computing and Applications 19, no. 6 (June 13, 2010): 787–805. http://dx.doi.org/10.1007/s00521-010-0387-3.

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47

Lachmair, J., E. Merényi, M. Porrmann, and U. Rückert. "A reconfigurable neuroprocessor for self-organizing feature maps." Neurocomputing 112 (July 2013): 189–99. http://dx.doi.org/10.1016/j.neucom.2012.11.045.

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48

Piadyk, Yurii, Bea Steers, Charlie Mydlarz, Mahin Salman, Magdalena Fuentes, Junaid Khan, Hong Jiang, Kaan Ozbay, Juan Pablo Bello, and Claudio Silva. "REIP: A Reconfigurable Environmental Intelligence Platform and Software Framework for Fast Sensor Network Prototyping." Sensors 22, no. 10 (May 17, 2022): 3809. http://dx.doi.org/10.3390/s22103809.

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Sensor networks have dynamically expanded our ability to monitor and study the world. Their presence and need keep increasing, and new hardware configurations expand the range of physical stimuli that can be accurately recorded. Sensors are also no longer simply recording the data, they process it and transform into something useful before uploading to the cloud. However, building sensor networks is costly and very time consuming. It is difficult to build upon other people’s work and there are only a few open-source solutions for integrating different devices and sensing modalities. We introduce REIP, a Reconfigurable Environmental Intelligence Platform for fast sensor network prototyping. REIP’s first and most central tool, implemented in this work, is an open-source software framework, an SDK, with a flexible modular API for data collection and analysis using multiple sensing modalities. REIP is developed with the aim of being user-friendly, device-agnostic, and easily extensible, allowing for fast prototyping of heterogeneous sensor networks. Furthermore, our software framework is implemented in Python to reduce the entrance barrier for future contributions. We demonstrate the potential and versatility of REIP in real world applications, along with performance studies and benchmark REIP SDK against similar systems.

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AL-Allaf, Ahmad, and A. I. A. Jabbar. "Reconfigurable Nonlinear GRED Algorithm." International Journal of Computing and Digital Systems 9, no. 5 (September 1, 2020): 1009–22. http://dx.doi.org/10.12785/ijcds/090521.

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Aunet, S., B. Oelmann, P. A. Norseng, and Y. Berg. "Real-Time Reconfigurable Subthreshold CMOS Perceptron." IEEE Transactions on Neural Networks 19, no. 4 (April 2008): 645–57. http://dx.doi.org/10.1109/tnn.2007.912572.

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